1
|
Abd Elaziz M, Dahou A, Aseeri AO, Ewees AA, Al-Qaness MAA, Ibrahim RA. Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment. Comput Biol Chem 2024; 111:108110. [PMID: 38815500 DOI: 10.1016/j.compbiolchem.2024.108110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/19/2024] [Accepted: 05/19/2024] [Indexed: 06/01/2024]
Abstract
The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.
Collapse
Affiliation(s)
- Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates; MEU Research Unit, Middle East University, Amman 11831, Jordan.
| | - Abdelghani Dahou
- Mathematics and Computer Science Department, University of Ahmed DRAIA, 01000, Adrar, Algeria; LDDI Laboratory, Faculty of Science and Technology, University of Ahmed DRAIA, 01000, Adrar, Algeria.
| | - Ahmad O Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt.
| | - Mohammed A A Al-Qaness
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China; Zhejiang Optoelectronics Research Institute, Jinhua 321004, China; College of Engineering and Information Technology, Emirates International University, Sana'a 16881, Yemen.
| | - Rehab Ali Ibrahim
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.
| |
Collapse
|
2
|
Hu G, Zheng Y, Houssein EH, Wei G. DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation. Comput Biol Med 2024; 178:108780. [PMID: 38909447 DOI: 10.1016/j.compbiomed.2024.108780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/06/2024] [Accepted: 06/15/2024] [Indexed: 06/25/2024]
Abstract
Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.
Collapse
Affiliation(s)
- Gang Hu
- Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, PR China.
| | - Yixuan Zheng
- Department of Applied Mathematics, Xi'an University of Technology, Xi'an, 710054, PR China
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Guo Wei
- University of North Carolina at Pembroke, Pembroke, NC, 28372, USA
| |
Collapse
|
3
|
Davoudkhani IF, Zare P, Abdelaziz AY, Bajaj M, Tuka MB. Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy storage. Sci Rep 2024; 14:13962. [PMID: 38886513 PMCID: PMC11183115 DOI: 10.1038/s41598-024-64794-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs and elevated power density requirements necessitate alternative strategies to balance power supply and demand. In recent years, substantial attention has turned towards harnessing Electric Vehicle (EV) batteries as Mobile EV Energy Storage (MEVES) units to counteract frequency variations in IUMGs. Integrating MEVES into the IUMG infrastructure introduces complexity and demands a robust control mechanism for optimal operation. Therefore, this paper introduces a robust, high-order degree of freedom cascade controller known as the 1PD-3DOF-PID (1 + Proportional + Derivative-Three Degrees Of Freedom Proportional-Integral-Derivative) controller for Load Frequency Control (LFC) in IUMGs integrated with MEVES. The controller's parameters are meticulously optimized using the Coati Optimization Algorithm (COA) which mimics coati behavior in nature, marking its debut in LFC of IUMG applications. Comparative evaluations against classical controllers and algorithms, such as 3DOF-PID, PID, Reptile Search Algorithm, and White Shark Optimizer, are conducted under diverse IUMG operating scenarios. The testbed comprises various renewable energy sources, including wind turbines, photovoltaics, Diesel Engine Generators (DEGs), Fuel Cells (FCs), and both Mobile and Fixed energy storage units. Managing power balance in this entirely renewable environment presents a formidable challenge, prompting an examination of the influence of MEVES, DEG, and FC as controllable units to mitigate active power imbalances. Metaheuristic algorithms in MATLAB-SIMULINK platforms are employed to identify the controller's gains across all case studies, ensuring the maintenance of IUMG system frequency within predefined limits. Simulation results convincingly establish the superiority of the proposed controller over other counterparts. Furthermore, the controller's robustness is rigorously tested under ± 25% variations in specific IUMG parameters, affirming its resilience. Statistical analyses reinforce the robust performance of the COA-based 1PD-3DOF-PID control method. This work highlights the potential of the COA Technique-optimized 1PD-3DOF-PID controller for IUMG control, marking its debut application in the LFC domain for IUMGs. This comprehensive study contributes valuable insights into enhancing the reliability and stability of Islanded Urban Microgrids while integrating Mobile EV Energy Storage, marking a significant advancement in the field of Load-Frequency Control.
Collapse
Affiliation(s)
| | - Peyman Zare
- Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Almoataz Y Abdelaziz
- Faculty of Engineering, Ain Shams University, Cairo, 11517, Egypt
- Faculty of Engineering and Technology, Future University in Egypt, Cairo, 11835, Egypt
| | - Mohit Bajaj
- Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India.
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
- Graphic Era Hill University, Dehradun, 248002, India.
| | - Milkias Berhanu Tuka
- Department of Electrical and Computer Engineering, College of Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
| |
Collapse
|
4
|
Bacanin N, Perisic M, Jovanovic G, Damaševičius R, Stanisic S, Simic V, Zivkovic M, Stojic A. The explainable potential of coupling hybridized metaheuristics, XGBoost, and SHAP in revealing toluene behavior in the atmosphere. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172195. [PMID: 38631643 DOI: 10.1016/j.scitotenv.2024.172195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024]
Abstract
Toluene is a neurotoxic aromatic hydrocarbon and one of the major representatives of volatile organic compounds, known for its abundance, adverse health effects, and role in the formation of other atmospheric pollutants like ozone. This research introduces the enhanced version of the reptile search metaheuristics algorithm which has been utilized to tune the extreme gradient boosting hyperparameters, to investigate toluene atmospheric behavior patterns and interactions with other polluting species within defined environmental conditions. The study is based on a two-year database encompassing concentrations of inorganic gaseous contaminants every hour (NO, NO2, NOx, and O3), particulate matter fractions (PM1, PM2.5, and PM10), m,p-xylene, toluene, benzene, total non-methane hydrocarbons, and meteorological data. The experimental outcomes were validated against the results of extreme gradient boosting models optimized by seven other recent powerful metaheuristics algorithms. The best-performing model has been interpreted by employing Shapley additive explanations method. In the study, we have focused on the relationship between toluene and benzene, as its most important predictor, and provided a detailed description of environmental conditions which directed their interactions.
Collapse
Affiliation(s)
- Nebojsa Bacanin
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Sinergija University, Raje Banjicica, Bjeljina 76300, Bosnia and Herzegovina.
| | - Mirjana Perisic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Belgrade 11010, Serbia.
| | - Gordana Jovanovic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Belgrade 11010, Serbia.
| | - Robertas Damaševičius
- Centre of Real Time Computer Systems, Kaunas University of Technology, Barsausko 59, Kaunas 51423, Lithuania.
| | - Svetlana Stanisic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia.
| | - Vladimir Simic
- Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade 44249, Serbia; Yuan Ze University, College of Engineering, Department of Industrial Engineering and Management, Taoyuan City 320315, Taiwan; Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea.
| | - Miodrag Zivkovic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia.
| | - Andreja Stojic
- Informatics and Computing, Singidunum University, Danijelova 32, Belgrade 11010, Serbia; Sinergija University, Raje Banjicica, Bjeljina 76300, Bosnia and Herzegovina.
| |
Collapse
|
5
|
Wang R, Zhang S, Zou G. An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems. Biomimetics (Basel) 2024; 9:361. [PMID: 38921241 PMCID: PMC11201394 DOI: 10.3390/biomimetics9060361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/09/2024] [Accepted: 06/12/2024] [Indexed: 06/27/2024] Open
Abstract
The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish's summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness-distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm's ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank-sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems.
Collapse
Affiliation(s)
- Ruitong Wang
- Leicester Institution, Dalian University of Technology, Dalian 124221, China; (R.W.); (S.Z.)
| | - Shuishan Zhang
- Leicester Institution, Dalian University of Technology, Dalian 124221, China; (R.W.); (S.Z.)
| | - Guangyu Zou
- Institute of Public Foundations, Dalian University of Technology, Dalian 124221, China
| |
Collapse
|
6
|
Cao Y, Liu Y, Tang L, Jiang Z, Liu Z, Zhou L, Yang B. Quantitative assessment of brain injury and concussion induced by an unintentional soccer ball impact. Injury 2024; 55:111658. [PMID: 38879923 DOI: 10.1016/j.injury.2024.111658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/21/2024] [Accepted: 06/03/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Accidental impact on a player's head by a powerful soccer ball may lead to brain injuries and concussions during games. It is crucial to assess these injuries promptly and accurately on the field. However, it is challenging for referees, coaches, and even players themselves to accurately recognize potential injuries and concussions following such impacts. Therefore, it is necessary to establish a list of minimum ball velocity thresholds that can result in concussions at different impact locations on the head. Additionally, it is important to identify the affected brain regions responsible for impairments in brain function and potential clinical symptoms. METHODS By using a full human finite element model, dynamic responses and brain injuries caused by unintentional soccer ball impacts on six distinct head locations (forehead, tempus, crown, occiput, face, and jaw) at varying ball velocities (10, 15, 20, 25, 30, 35, 40, and 60 m/s) were simulated and investigated. Intracranial pressure, Von-Mises stress, and first principal strain were analyzed, the ball velocity thresholds resulting in concussions at different impact locations were evaluated, and the damage evolution patterns in the brain tissue were analyzed. RESULTS The impact on the occiput is most susceptible to induce brain injuries compared to all other impact locations. For a conservative assessment, the risk of concussion is present once the soccer ball reaches 17.2 m/s in a frontal impact, 16.6 m/s in a parietal impact, 14.0 m/s in an occipital impact, 17.8 m/s in a temporal impact, 18.5 m/s in a facial impact or 19.2 m/s in a mandibular impact. The brain exhibits the most significant dynamic responses during the initial 10-20 ms, and the damaged regions are primarily concentrated in the medial temporal lobe and the corpus callosum, potentially causing impairments in brain functions. CONCLUSIONS This work offers a framework for quantitatively assessing brain injuries and concussions induced by an unintentional soccer ball impact. Determining the ball velocity thresholds at various impact locations provides a benchmark for evaluating the risks of concussion. The examination of brain tissue damage evolution introduces a novel approach to linking biomechanical responses with possible clinical symptoms.
Collapse
Affiliation(s)
- Yangjian Cao
- School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China
| | - Yiping Liu
- School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China.
| | - Liqun Tang
- School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China
| | - Zhenyu Jiang
- School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China
| | - Zejia Liu
- School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China
| | - Licheng Zhou
- School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China
| | - Bao Yang
- School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou 510640, China
| |
Collapse
|
7
|
Jovanovic L, Damaševičius R, Matic R, Kabiljo M, Simic V, Kunjadic G, Antonijevic M, Zivkovic M, Bacanin N. Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics. PeerJ Comput Sci 2024; 10:e2031. [PMID: 38855236 PMCID: PMC11157549 DOI: 10.7717/peerj-cs.2031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/09/2024] [Indexed: 06/11/2024]
Abstract
Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.
Collapse
Affiliation(s)
- Luka Jovanovic
- Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia
| | | | - Rade Matic
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Milos Kabiljo
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Vladimir Simic
- Faculty of Transport and Traffic Engineering, University of Belgrade, Belgrade, Serbia
- College of Engineering, Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan City, Taiwan
| | - Goran Kunjadic
- Higher Colleges of Technology, Abu Dhabi, United Arab Emirates
| | - Milos Antonijevic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
- MEU Research Unit, Middle East University, Amman, Jordan
| |
Collapse
|
8
|
Zhang K, He Y, Wang Y, Sun C. Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization. Biomimetics (Basel) 2024; 9:280. [PMID: 38786490 PMCID: PMC11118958 DOI: 10.3390/biomimetics9050280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm's efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness-distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm's escape from local optima. Finally, a best-worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm's exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance.
Collapse
Affiliation(s)
- Kuan Zhang
- College of Information Science and Technology, Northeastern University, Shenyang 110000, China; (K.Z.); (Y.H.)
- School of Aerospace, Harbin Institute of Technology, Harbin 150001, China
| | - Yirui He
- College of Information Science and Technology, Northeastern University, Shenyang 110000, China; (K.Z.); (Y.H.)
| | - Yuhang Wang
- School of Software, Henan University, Kaifeng 475001, China;
| | - Changjian Sun
- College of Electronic Science and Engineering, Jilin University, Changchun 130000, China
| |
Collapse
|
9
|
Houssein EH, Hammad A, Emam MM, Ali AA. An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition. Comput Biol Med 2024; 173:108329. [PMID: 38513391 DOI: 10.1016/j.compbiomed.2024.108329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 03/23/2024]
Abstract
Emotion recognition based on Electroencephalography (EEG) signals has garnered significant attention across diverse domains including healthcare, education, information sharing, and gaming, among others. Despite its potential, the absence of a standardized feature set poses a challenge in efficiently classifying various emotions. Addressing the issue of high dimensionality, this paper introduces an advanced variant of the Coati Optimization Algorithm (COA), called eCOA for global optimization and selecting the best subset of EEG features for emotion recognition. Specifically, COA suffers from local optima and imbalanced exploitation abilities as other metaheuristic methods. The proposed eCOA incorporates the COA and RUNge Kutta Optimizer (RUN) algorithms. The Scale Factor (SF) and Enhanced Solution Quality (ESQ) mechanism from RUN are applied to resolve the raised shortcomings of COA. The proposed eCOA algorithm has been extensively evaluated using the CEC'22 test suite and two EEG emotion recognition datasets, DEAP and DREAMER. Furthermore, the eCOA is applied for binary and multi-class classification of emotions in the dimensions of valence, arousal, and dominance using a multi-layer perceptron neural network (MLPNN). The experimental results revealed that the eCOA algorithm has more powerful search capabilities than the original COA and seven well-known counterpart methods related to statistical, convergence, and diversity measures. Furthermore, eCOA can efficiently support feature selection to find the best EEG features to maximize performance on four quadratic emotion classification problems compared to the methods of its counterparts. The suggested method obtains a classification accuracy of 85.17% and 95.21% in the binary classification of low and high arousal emotions in two public datasets: DEAP and DREAMER, respectively, which are 5.58% and 8.98% superior to existing approaches working on the same datasets for different subjects, respectively.
Collapse
Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Asmaa Hammad
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Abdelmgeid A Ali
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| |
Collapse
|
10
|
Varshney M, Kumar P, Ali M, Gulzar Y. Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems. Biomimetics (Basel) 2024; 9:215. [PMID: 38667226 PMCID: PMC11047905 DOI: 10.3390/biomimetics9040215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.
Collapse
Affiliation(s)
- Megha Varshney
- Rajkiya Engineering College (AKTU, Lucknow), Bijnor 246725, India; (M.V.)
| | - Pravesh Kumar
- Rajkiya Engineering College (AKTU, Lucknow), Bijnor 246725, India; (M.V.)
| | - Musrrat Ali
- Department of Basic Sciences, Preparatory Year, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Yonis Gulzar
- Department of Management Information Systems, College of Business Administration, King Faisal University, Al Ahsa 31982, Saudi Arabia;
| |
Collapse
|
11
|
Yan J, Hu G, Zhang J. Multi-Strategy Boosted Fick's Law Algorithm for Engineering Optimization Problems and Parameter Estimation. Biomimetics (Basel) 2024; 9:205. [PMID: 38667216 PMCID: PMC11048509 DOI: 10.3390/biomimetics9040205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
To address the shortcomings of the recently proposed Fick's Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick's Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation strategy, interweaving-based comprehensive learning strategy, and seagull update strategy. First, the differential variation strategy is added in the search phase to increase the randomness and expand the search degree of space. Second, by introducing the Gaussian local variation, the search diversity is increased, and the exploration capability and convergence efficiency are further improved. Further, a comprehensive learning strategy that simultaneously updates multiple individual parameters is introduced to improve search diversity and shorten the running time. Finally, the stability of the update is improved by adding a global search mechanism to balance the distribution of molecules on both sides during seagull updates. To test the competitiveness of the algorithms, the exploration and exploitation capability of the proposed FLAS is validated on 23 benchmark functions, and CEC2020 tests. FLAS is compared with other algorithms in seven engineering optimizations such as a reducer, three-bar truss, gear transmission system, piston rod optimization, gas transmission compressor, pressure vessel, and stepped cone pulley. The experimental results verify that FLAS can effectively optimize conventional engineering optimization problems. Finally, the engineering applicability of the FLAS algorithm is further highlighted by analyzing the results of parameter estimation for the solar PV model.
Collapse
Affiliation(s)
- Jialing Yan
- Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China;
| | - Gang Hu
- Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China;
| | - Jiulong Zhang
- Computer Network Information Center, Xi’an University of Technology, Xi’an 710048, China;
| |
Collapse
|
12
|
Zhang L, Chen X. Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification. Sci Rep 2024; 14:6910. [PMID: 38519568 PMCID: PMC10959962 DOI: 10.1038/s41598-024-57518-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 03/19/2024] [Indexed: 03/25/2024] Open
Abstract
Feature selection is a critical component of machine learning and data mining to remove redundant and irrelevant features from a dataset. The Chimp Optimization Algorithm (CHoA) is widely applicable to various optimization problems due to its low number of parameters and fast convergence rate. However, CHoA has a weak exploration capability and tends to fall into local optimal solutions in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. To solve this problem, this paper proposes the Enhanced Chimp Hierarchy Optimization Algorithm for adaptive lens imaging (ALI-CHoASH) for searching the optimal classification problems for the optimal subset of features. Specifically, to enhance the exploration and exploitation capability of CHoA, we designed a chimp social hierarchy. We employed a novel social class factor to label the class situation of each chimp, enabling effective modelling and optimization of the relationships among chimp individuals. Then, to parse chimps' social and collaborative behaviours with different social classes, we introduce other attacking prey and autonomous search strategies to help chimp individuals approach the optimal solution faster. In addition, considering the poor diversity of chimp groups in the late iteration, we propose an adaptive lens imaging back-learning strategy to avoid the algorithm falling into a local optimum. Finally, we validate the improvement of ALI-CHoASH in exploration and exploitation capabilities using several high-dimensional datasets. We also compare ALI-CHoASH with eight state-of-the-art methods in classification accuracy, feature subset size, and computation time to demonstrate its superiority.
Collapse
Affiliation(s)
- Li Zhang
- College of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, People's Republic of China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University, Changchun, 130012, People's Republic of China.
| | - XiaoBo Chen
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University, Changchun, 130012, People's Republic of China
- People's Bank of China Changzhou City Center Branch, Changzhou, 213001, Jiangsu, People's Republic of China
| |
Collapse
|
13
|
Khanna M, Singh LK, Shrivastava K, Singh R. An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study. Heliyon 2024; 10:e26799. [PMID: 38463826 PMCID: PMC10920178 DOI: 10.1016/j.heliyon.2024.e26799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 01/15/2024] [Accepted: 02/20/2024] [Indexed: 03/12/2024] Open
Abstract
Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.
Collapse
Affiliation(s)
- Munish Khanna
- School of Computing Science and Engineering, Galgotias University, Greater Noida, Gautam Buddh Nagar, India
| | - Law Kumar Singh
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Kapil Shrivastava
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Rekha Singh
- Department of Physics, Uttar Pradesh Rajarshi Tandon Open University, Prayagraj, Uttar Pradesh, India
| |
Collapse
|
14
|
Saraswat M, Dubey AK. EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface. Comput Methods Biomech Biomed Engin 2024; 27:378-399. [PMID: 36951376 DOI: 10.1080/10255842.2023.2187662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/26/2022] [Accepted: 03/01/2023] [Indexed: 03/24/2023]
Abstract
Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain-computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.
Collapse
Affiliation(s)
- Mala Saraswat
- Assistant Professor, School of Computing Science and Engineering, Bennett University, Noida, India
| | - Anil Kumar Dubey
- Associate Professor, CSE Department, ABES Engineering College Ghaziabad, Ghaziabad, India
| |
Collapse
|
15
|
Amiri MH, Mehrabi Hashjin N, Montazeri M, Mirjalili S, Khodadadi N. Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Sci Rep 2024; 14:5032. [PMID: 38424229 PMCID: PMC10904400 DOI: 10.1038/s41598-024-54910-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 02/18/2024] [Indexed: 03/02/2024] Open
Abstract
The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .
Collapse
Affiliation(s)
| | | | - Mohsen Montazeri
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Adelaide, Australia
- Research and Innovation Center, Obuda University, Budapest, 1034, Hungary
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA
| |
Collapse
|
16
|
Hubálovská M, Hubálovský Š, Trojovský P. Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics (Basel) 2024; 9:137. [PMID: 38534822 DOI: 10.3390/biomimetics9030137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA's ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA's superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA's effectiveness in real-world optimization tasks.
Collapse
Affiliation(s)
- Marie Hubálovská
- Department of Technics, Faculty of Education, University of Hradec Kralove, 50003 Hradec Králové, Czech Republic
| | - Štěpán Hubálovský
- Department of Technics, Faculty of Education, University of Hradec Kralove, 50003 Hradec Králové, Czech Republic
| | - Pavel Trojovský
- Department of Technics, Faculty of Education, University of Hradec Kralove, 50003 Hradec Králové, Czech Republic
| |
Collapse
|
17
|
Kumar P, Ali M. Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics. Biomimetics (Basel) 2024; 9:119. [PMID: 38392164 PMCID: PMC10887041 DOI: 10.3390/biomimetics9020119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/26/2024] [Accepted: 02/10/2024] [Indexed: 02/24/2024] Open
Abstract
The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as "Improved DE with Best and Worst positions (IDEBW)", offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient.
Collapse
Affiliation(s)
- Pravesh Kumar
- ASH (Mathematics) Department, REC Bijnor, Chandpur 246725, UP, India
| | - Musrrat Ali
- Department of Basic Sciences, Preparatory Year, King Faisal University, Al Ahsa 31982, Saudi Arabia
| |
Collapse
|
18
|
Houssein EH, Abdalkarim N, Hussain K, Mohamed E. Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease. Comput Biol Med 2024; 169:107922. [PMID: 38184861 DOI: 10.1016/j.compbiomed.2024.107922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/19/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Liver-related diseases significantly contribute to global mortality rates. Accurate segmentation of liver disease from CT scans is essential for early diagnosis and treatment selection, particularly in computer-aided diagnosis (CAD) systems. To address challenges posed by inconsistent liver presence and unclear boundaries, an enhanced Snake Optimization (SO) algorithm is proposed that integrates with opposition-based learning (OBL) called (SO-OBL), proving effective in global optimization and multilevel image segmentation. Experiments using CEC'2022 test functions compare SO-OBL with eleven recent and state-of-the-art metaheuristic algorithms, demonstrating its superior performance. Additionally, an advanced liver disease segmentation model based on SO-OBL incorporates an optimized multilevel thresholding technique, leveraging Otsu's function. Notable segmentation metric results, including FSIM = 0.947, SSIM = 0.941, PSNR = 24.876, MSE = 236.88, and execution time = 0.281, underscore the model's efficiency and potential for accurate diagnosis in CAD systems.
Collapse
Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Nada Abdalkarim
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Kashif Hussain
- Department of Science and Engineering, Solent University, Southampton, United Kingdom.
| | - Ebtsam Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| |
Collapse
|
19
|
Hassan MU, Al-Awady AA, Ali A, Iqbal MM, Akram M, Jamil H. Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:865. [PMID: 38339582 PMCID: PMC10857058 DOI: 10.3390/s24030865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024]
Abstract
Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users' context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).
Collapse
Affiliation(s)
- Mahmood Ul Hassan
- Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran 66241, Saudi Arabia; (M.U.H.); (A.A.A.-A.)
| | - Amin A. Al-Awady
- Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran 66241, Saudi Arabia; (M.U.H.); (A.A.A.-A.)
| | - Abid Ali
- Department of Computer Science, University of Engineering and Technology, Taxila 48080, Pakistan;
- Department of Computer Science, Govt. A.N.K. (S) Degree College K.T.S., Haripur 22620, Pakistan
| | - Muhammad Munwar Iqbal
- Department of Computer Science, University of Engineering and Technology, Taxila 48080, Pakistan;
| | - Muhammad Akram
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66241, Saudi Arabia;
| | - Harun Jamil
- Department of Electronic Engineering, Jeju National University, Jeju-si 63243, Republic of Korea;
| |
Collapse
|
20
|
Al-Baik O, Alomari S, Alssayed O, Gochhait S, Leonova I, Dutta U, Malik OP, Montazeri Z, Dehghani M. Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics (Basel) 2024; 9:65. [PMID: 38392111 PMCID: PMC10887113 DOI: 10.3390/biomimetics9020065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/10/2024] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator's attack on a pufferfish and (ii) exploitation based on the simulation of a predator's escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms.
Collapse
Affiliation(s)
- Osama Al-Baik
- Department of Software Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
| | - Saleh Alomari
- ISBM COE, Faculty of Science and Information Technology, Software Engineering, Jadara University, Irbid 21110, Jordan
| | - Omar Alssayed
- Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
| | - Saikat Gochhait
- Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University, Pune 412115, India
- Neuroscience Research Institute, Samara State Medical University, 89 Chapaevskaya Street, 443001 Samara, Russia
| | - Irina Leonova
- Neuroscience Research Institute, Samara State Medical University, 89 Chapaevskaya Street, 443001 Samara, Russia
- Faculty of Social Sciences, Lobachevsky University, 603950 Nizhny Novgorod, Russia
| | - Uma Dutta
- Former Dean of Life Sciences and Head of Zoology Department, Celland Molecular Biology, Toxicology Laboratory, Department of Zoology, Cotton University, Guwahati 781001, India
| | - Om Parkash Malik
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Zeinab Montazeri
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
| | - Mohammad Dehghani
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
| |
Collapse
|
21
|
Varshney M, Kumar P, Ali M, Gulzar Y. Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering. Biomimetics (Basel) 2024; 9:54. [PMID: 38248628 PMCID: PMC10813268 DOI: 10.3390/biomimetics9010054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024] Open
Abstract
The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific situations. To increase the exploration ability of the AO algorithm, this work offers a hybrid approach that employs the alpha position of the Grey Wolf Optimizer (GWO) to drive the search process of the AO algorithm. At the same time, we applied the quasi-opposition-based learning (QOBL) strategy in each phase of the Aquila Optimizer algorithm. This strategy develops quasi-oppositional solutions to current solutions. The quasi-oppositional solutions are then utilized to direct the search phase of the AO algorithm. The GWO method is also notable for its resistance to noise. This means that it can perform effectively even when the objective function is noisy. The AO algorithm, on the other hand, may be sensitive to noise. By integrating the GWO approach into the AO algorithm, we can strengthen its robustness to noise, and hence, improve its performance in real-world issues. In order to evaluate the effectiveness of the technique, the algorithm was benchmarked on 23 well-known test functions and CEC2017 test functions and compared with other popular metaheuristic algorithms. The findings demonstrate that our proposed method has excellent efficacy. Finally, it was applied to five practical engineering issues, and the results showed that the technique is suitable for tough problems with uncertain search spaces.
Collapse
Affiliation(s)
- Megha Varshney
- Rajkiya Engineering College, Dr. APJ Abdul Kalam Kalam Technical University, Bijnor 246725, India
| | - Pravesh Kumar
- Rajkiya Engineering College, Dr. APJ Abdul Kalam Kalam Technical University, Bijnor 246725, India
| | - Musrrat Ali
- Department of Basic Sciences, General Administration of Preparatory Year, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Yonis Gulzar
- Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| |
Collapse
|
22
|
Huang J, Hu H. Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design. Biomimetics (Basel) 2024; 9:21. [PMID: 38248595 PMCID: PMC11154476 DOI: 10.3390/biomimetics9010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/12/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024] Open
Abstract
In this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a dynamic opposite learning strategy broadens the search area of the population, enhances global search ability, and improves population diversity. In the honey harvesting stage of the honey badger (development), differential mutation strategies are combined, selectively introducing local quantum search strategies that enhance local search capabilities and improve population optimization accuracy, or introducing dynamic Laplacian crossover operators that can improve convergence speed, while reducing the odds of the HBA sinking into local optima. Through comparative experiments with other algorithms on the CEC2017, CEC2020, and CEC2022 test sets, and three engineering examples, EHBA has been verified to have good solving performance. From the comparative analysis of convergence graphs, box plots, and algorithm performance tests, it can be seen that compared with the other eight algorithms, EHBA has better results, significantly improving its optimization ability and convergence speed, and has good application prospects in the field of optimization problems.
Collapse
Affiliation(s)
- Jiaxu Huang
- School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China;
| | | |
Collapse
|
23
|
Chen K, Weng Y, Hosseini AA, Dening T, Zuo G, Zhang Y. A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer's Disease involving data synthesis. Neural Netw 2024; 169:442-452. [PMID: 37939533 DOI: 10.1016/j.neunet.2023.10.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/23/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disease that commonly occurs in older people. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). This paper proposes a U-Net based Generative Adversarial Network (GAN) to synthesize fluorodeoxyglucose - positron emission tomography (FDG-PET) from magnetic resonance imaging - T1 weighted imaging (MRI-T1WI) for further usage in AD diagnosis including its early-stage MCI. The experiments have displayed promising results with Structural Similarity Index Measure (SSIM) reaching 0.9714. Furthermore, three types of classifiers are developed, i.e., one Multi-Layer Perceptron (MLP) based classifier, two Graph Neural Network (GNN) based classifiers where one is for graph classification and the other is for node classification. 10-fold cross-validation has been conducted on all trials of experiments for classifier comparison. The performance of these three types of classifiers has been compared with the different input modalities setting and data fusion strategies. The results have shown that GNN based node classifier surpasses the other two types of classifiers, and has achieved the state-of-the-art (SOTA) performance with the best accuracy at 90.18% for 3-class classification, namely AD, MCI and normal control (NC) with the synthesized fluorodeoxyglucose - positron emission tomography (FDG-PET) features fused at the input level. Moreover, involving synthesized FDG-PET as part of the input with proper data fusion strategies has also proved to enhance all three types of classifiers' performance. This work provides support for the notion that machine learning-derived image analysis may be a useful approach to improving the diagnosis of AD.
Collapse
Affiliation(s)
- Ke Chen
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China; Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China; School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK.
| | - Ying Weng
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China.
| | - Akram A Hosseini
- Neurology Department, Nottingham University Hospitals NHS Trust, Nottingham, NG7 2UH, UK.
| | - Tom Dening
- School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK.
| | - Guokun Zuo
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
| | - Yiming Zhang
- School of Computer Science, University of Nottingham Ningbo China, Ningbo, 315100, China; School of Medicine, University of Nottingham, Nottingham, NG7 2RD, UK.
| |
Collapse
|
24
|
Hubálovský Š, Hubálovská M, Matoušová I. A New Hybrid Particle Swarm Optimization-Teaching-Learning-Based Optimization for Solving Optimization Problems. Biomimetics (Basel) 2023; 9:8. [PMID: 38248582 PMCID: PMC10813294 DOI: 10.3390/biomimetics9010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/09/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024] Open
Abstract
This research paper develops a novel hybrid approach, called hybrid particle swarm optimization-teaching-learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of "exploitation capabilities of PSO" is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, "exploration abilities of TLBO" means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications.
Collapse
Affiliation(s)
- Štěpán Hubálovský
- Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003 Hradec Kralove, Czech Republic
| | - Marie Hubálovská
- Department of Technics, Faculty of Education, University of Hradec Králové, 50003 Hradec Kralove, Czech Republic;
| | - Ivana Matoušová
- Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Kralove, Czech Republic;
| |
Collapse
|
25
|
Minic A, Jovanovic L, Bacanin N, Stoean C, Zivkovic M, Spalevic P, Petrovic A, Dobrojevic M, Stoean R. Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:9878. [PMID: 38139724 PMCID: PMC10747899 DOI: 10.3390/s23249878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/28/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023]
Abstract
Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.
Collapse
Affiliation(s)
- Ana Minic
- Teacher Education Faculty, University of Pristina in Kosovska Mitrovica, 38220 Kosovska Mitrovica, Serbia;
| | - Luka Jovanovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Catalin Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Petar Spalevic
- Faculty of Technical Science, University of Pristina in Kosovska Mitrovica, Filipa Visnjica bb, 38220 Kosovska Mitrovica, Serbia;
| | - Aleksandar Petrovic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Milos Dobrojevic
- Faculty of Informatics and Computing, Singidunum University, 160622 Belgrade, Serbia; (L.J.); (N.B.); (M.Z.); (A.P.); (M.D.)
| | - Ruxandra Stoean
- Department of Computer Science, Faculty of Sciences, University of Craiova, 200585 Craiova, Romania;
| |
Collapse
|
26
|
Alsayyed O, Hamadneh T, Al-Tarawneh H, Alqudah M, Gochhait S, Leonova I, Malik OP, Dehghani M. Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics (Basel) 2023; 8:619. [PMID: 38132558 PMCID: PMC10741582 DOI: 10.3390/biomimetics8080619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos' digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.
Collapse
Affiliation(s)
- Omar Alsayyed
- Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan;
| | - Tareq Hamadneh
- Department of Matematics, Al Zaytoonah University of Jordan, Amman 11733, Jordan;
| | - Hassan Al-Tarawneh
- Department of Data Sciences and Artificial Intelligence, Al-Ahliyya Amman University, Amman 11942, Jordan;
| | - Mohammad Alqudah
- Department of Basic Sciences, German Jordanian University, Amman 11180, Jordan;
| | - Saikat Gochhait
- Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University, Pune 412115, India;
- Neuroscience Research Institute, Samara State Medical University, 89 Chapaevskaya str., 443001 Samara, Russia;
| | - Irina Leonova
- Neuroscience Research Institute, Samara State Medical University, 89 Chapaevskaya str., 443001 Samara, Russia;
- Faculty of Social Sciences, Lobachevsky University, 603950 Nizhny Novgorod, Russia
| | - Om Parkash Malik
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Mohammad Dehghani
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
| |
Collapse
|
27
|
Kailasam JK, Nalliah R, Nallagoundanpalayam Muthusamy S, Manoharan P. MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems. Biomimetics (Basel) 2023; 8:615. [PMID: 38132554 PMCID: PMC10741723 DOI: 10.3390/biomimetics8080615] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.
Collapse
Affiliation(s)
- Jeyaganesh Kumar Kailasam
- Department of Artificial Intelligence and Data Science, M. Kumarasamy College of Engineering, Karur 639113, Tamilnadu, India
| | - Rajkumar Nalliah
- Department of Computer Science and Engineering, KGiSL Institute of Technology, Coimbatore 641035, Tamilnadu, India;
| | | | - Premkumar Manoharan
- Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bangalore 560078, Karnataka, India
| |
Collapse
|
28
|
Trojovský P. A new human-based metaheuristic algorithm for solving optimization problems based on preschool education. Sci Rep 2023; 13:21472. [PMID: 38052945 PMCID: PMC10697988 DOI: 10.1038/s41598-023-48462-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/27/2023] [Indexed: 12/07/2023] Open
Abstract
In this paper, with motivation from the No Free Lunch theorem, a new human-based metaheuristic algorithm named Preschool Education Optimization Algorithm (PEOA) is introduced for solving optimization problems. Human activities in the preschool education process are the fundamental inspiration in the design of PEOA. Hence, PEOA is mathematically modeled in three phases: (i) the gradual growth of the preschool teacher's educational influence, (ii) individual knowledge development guided by the teacher, and (iii) individual increase of knowledge and self-awareness. The PEOA's performance in optimization is evaluated using fifty-two standard benchmark functions encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, as well as the CEC 2017 test suite. The optimization results show that PEOA has a high ability in exploration-exploitation and can balance them during the search process. To provide a comprehensive analysis, the performance of PEOA is compared against ten well-known metaheuristic algorithms. The simulation results show that the proposed PEOA approach performs better than competing algorithms by providing effective solutions for the benchmark functions and overall ranking as the first-best optimizer. Presenting a statistical analysis of the Wilcoxon signed-rank test shows that PEOA has significant statistical superiority in competition with compared algorithms. Furthermore, the implementation of PEOA in solving twenty-two optimization problems from the CEC 2011 test suite and four engineering design problems illustrates its efficacy in real-world optimization applications.
Collapse
Affiliation(s)
- Pavel Trojovský
- Department of Mathematics, Faculty of Science, University of Hradec Králové, Rokitanského 62, 500 03, Hradec Králové, Czech Republic.
| |
Collapse
|
29
|
Zhang X, Zhang W, Sun W, Song A, Xu T. A high-fidelity virtual liver model incorporating biological characteristics. Heliyon 2023; 9:e22978. [PMID: 38125508 PMCID: PMC10731058 DOI: 10.1016/j.heliyon.2023.e22978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023] Open
Abstract
Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. However, most existing models lack the unique biological characteristics. To solve this problem, we proposed a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics. Besides, to the best of our knowledge, our study is the first to introduce the viscoplasticity of biological tissues to improve the fidelity of the liver model. This mothod was proposed to describe the viscoplastic characteristics of the diseased liver resection process, when the liver is in a state of excessive deformation and loss of elasticity, however, there are few works focusing on this problem. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can enhance visual perception ability, improve deformation accuracy and fidelity.
Collapse
Affiliation(s)
- Xiaorui Zhang
- School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Wenzheng Zhang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- School of Software, Nanjing University, Nanjing, 210093, China
| | - Wei Sun
- College of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Aiguo Song
- College of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Tong Xu
- University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
30
|
Pilcevic D, Djuric Jovicic M, Antonijevic M, Bacanin N, Jovanovic L, Zivkovic M, Dragovic M, Bisevac P. Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection. Front Physiol 2023; 14:1267011. [PMID: 38033337 PMCID: PMC10682794 DOI: 10.3389/fphys.2023.1267011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results' interpretation is performed.
Collapse
Affiliation(s)
- Dejan Pilcevic
- Clinic for Nephrology, Military Medical Academy, University of Defense, Belgrade, Serbia
| | | | - Milos Antonijevic
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Nebojsa Bacanin
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Luka Jovanovic
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | | | - Petar Bisevac
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| |
Collapse
|
31
|
Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alrizq M, Abosaq H, Alghamdi A. The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers' reviews. Heliyon 2023; 9:e21828. [PMID: 38034804 PMCID: PMC10682139 DOI: 10.1016/j.heliyon.2023.e21828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Customer Relationship Management (CRM) is a method of management that aims to establish, develop, and improve relationships with targeted customers in order to maximize corporate profitability and customer value. There have been many CRM systems in the market. These systems are developed based on the combination of business requirements, customer needs, and industry best practices. The impact of CRM systems on the customers' satisfaction and competitive advantages as well as tangible and intangible benefits are widely investigated in the previous studies. However, there is a lack of studies to assess the quality dimensions of these systems to meet an organization's CRM strategy. This study aims to investigate customers' satisfaction with CRM systems through online reviews. We collected 5172 online customers' reviews from 8 CRM systems in the Google play store platform. The satisfaction factors were extracted using Latent Dirichlet Allocation (LDA) and grouped into three dimensions; information quality, system quality, and service quality. Data segmentation is performed using Learning Vector Quantization (LVQ). In addition, feature selection is performed by the entropy-weight approach. We then used the Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of fuzzy logic and neural networks, to assess the relationship between these dimensions and customer satisfaction. The results are discussed and research implications are provided.
Collapse
Affiliation(s)
- Mehrbakhsh Nilashi
- UCSI Graduate Business School, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia
- Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800, Penang, Malaysia
| | - Rabab Ali Abumalloh
- Department of Computer Science and Engineering, Qatar University, Doha, 2713, Qatar
| | - Hossein Ahmadi
- Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK
| | - Sarminah Samad
- Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Mesfer Alrizq
- Information Systems Dept. College of Computer Science and Information Systems Najran University, Najran, Saudi Arabia
| | - Hamad Abosaq
- Computer Science Dept. College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Abdullah Alghamdi
- Information Systems Dept. College of Computer Science and Information Systems Najran University, Najran, Saudi Arabia
| |
Collapse
|
32
|
Hubalovska M, Major S. A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems Based on Technical and Vocational Education and Training. Biomimetics (Basel) 2023; 8:508. [PMID: 37887639 PMCID: PMC10604091 DOI: 10.3390/biomimetics8060508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/14/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational education and training schools. The theory of TVETBO is expressed and mathematically modeled in three phases: (i) theory education, (ii) practical education, and (iii) individual skills development. The performance of TVETBO when solving optimization problems is evaluated on the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that TVETBO, with its high abilities to explore, exploit, and create a balance between exploration and exploitation during the search process, is able to provide effective solutions for the benchmark functions. The results obtained from TVETBO are compared with the performances of twelve well-known metaheuristic algorithms. A comparison of the simulation results and statistical analysis shows that the proposed TVETBO approach provides better results in most of the benchmark functions and provides a superior performance in competition with competitor algorithms. Furthermore, in order to measure the effectiveness of the proposed approach in dealing with real-world applications, TVETBO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite. The simulation results show that TVETBO provides an effective and superior performance when solving constrained optimization problems of real-world applications compared to competitor algorithms.
Collapse
Affiliation(s)
- Marie Hubalovska
- Department of Technics, Faculty of Education, University of Hradec Kralove, CZ50003 Hradec Kralove, Czech Republic;
| | | |
Collapse
|
33
|
Dehghani M, Bektemyssova G, Montazeri Z, Shaikemelev G, Malik OP, Dhiman G. Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics (Basel) 2023; 8:507. [PMID: 37887638 PMCID: PMC10604244 DOI: 10.3390/biomimetics8060507] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.
Collapse
Affiliation(s)
- Mohammad Dehghani
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran;
| | - Gulnara Bektemyssova
- Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan;
| | - Zeinab Montazeri
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran;
| | - Galymzhan Shaikemelev
- Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan;
| | - Om Parkash Malik
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Gaurav Dhiman
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon;
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India
- Division of Research and Development, Lovely Professional University, Phagwara 144411, India
| |
Collapse
|
34
|
Dehghani M, Montazeri Z, Bektemyssova G, Malik OP, Dhiman G, Ahmed AEM. Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics (Basel) 2023; 8:470. [PMID: 37887601 PMCID: PMC10604064 DOI: 10.3390/biomimetics8060470] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 09/16/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications.
Collapse
Affiliation(s)
- Mohammad Dehghani
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran;
| | - Zeinab Montazeri
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran;
| | - Gulnara Bektemyssova
- Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan;
| | - Om Parkash Malik
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Gaurav Dhiman
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon;
- University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India
- Division of Research and Development, Lovely Professional University, Phagwara 144411, India
| | - Ayman E. M. Ahmed
- Faculty of Computer Engineering, King Salman International University, El Tor 46511, Egypt;
| |
Collapse
|
35
|
Zhang B, Wang Z, Ling Y, Guan Y, Zhang S, Li W, Wei L, Zhang C. ShuffleTrans: Patch-wise weight shuffle for transparent object segmentation. Neural Netw 2023; 167:199-212. [PMID: 37659116 DOI: 10.1016/j.neunet.2023.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/19/2023] [Accepted: 08/06/2023] [Indexed: 09/04/2023]
Abstract
Transparent objects widely exist in the world. The task of transparent object segmentation is challenging as the object lacks its own texture. The cue of shape information therefore gets more critical. Most existing methods, however, rely on the mechanism of simple convolution, which is good at local cues and performs weakly on global cues like shape. To solve this problem, an operation named Patch-wise Weight Shuffle is proposed to bring in the global context cue by being combined with the dynamic convolution. A network ShuffleTrans that recognizes shape better is then designed based on this operation. Besides, fitter for this task, two auxiliary modules are presented in ShuffleTrans: a Boundary and Direction Refinement Module which collects two additional information, and a Channel Attention Enhancement Module that assists the above operation. Experiments on four texture-less object segmentation datasets and two normal datasets verify the effectiveness and generality of the method. Especially, the ShuffleTrans achieved 74.93% mIoU on the Trans10k v2 test set, which is more accurate than existing methods.
Collapse
Affiliation(s)
- Boxiang Zhang
- College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineer, Jilin University, China.
| | | | | | - Yuanyuan Guan
- College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineer, Jilin University, China.
| | | | - Wenhui Li
- College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineer, Jilin University, China.
| | | | - Chunxu Zhang
- College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineer, Jilin University, China; City University of Hong Kong, Hong Kong.
| |
Collapse
|
36
|
Gong L, Chen J, Cui X, Liu Y. RPIPCM: A deep network model for predicting lncRNA-protein interaction based on sequence feature encoding. Comput Biol Med 2023; 165:107366. [PMID: 37633089 DOI: 10.1016/j.compbiomed.2023.107366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/29/2023] [Accepted: 08/12/2023] [Indexed: 08/28/2023]
Abstract
LncRNA-protein interactionplays an important regulatory role in biological processes. In this paper, the proposed RPIPCM based on a novel deep network model uses the sequence feature encoding of both RNA and protein to predict lncRNA-protein interactions (LPIs). A negative sampling of sliding window method is proposed for solving the problem of unbalanced between positive and negative samples. The proposed negative sampling method is effective and helpful to solve the problem of data imbalance in the existing LPIs research by comparative experiments. Experimental results also show that the proposed sequence feature encoding method has good performance in predicting LPIs for different datasets of different sizes and types. In the RPI488 dataset related to animal, compared with the direct original sequence encoding model, the accuracy of sequence feature encoding model increased by 1.02%, the recall increased by 4.08%, and the value of MCC increased by 1.67%. In the case of the plant dataset ATH948, the sequence feature-based encoding demonstrated a 1.58% higher accuracy, a 1.53% higher recall, a 1.62% higher specificity, a 1.62% higher precision, and a 3.16% higher value of MCC compared to the direct original sequence-based encoding. Compared with the latest prediction work in the ZEA22133 dataset, RPIPCM is shown to be more effective with the accuracy increased by 2.23%, the recall increased by 1.78%, the specificity increased by 2.67%, the precision increased by 2.52%, and the value of MCC increased by 4.43%, which also proves the effectiveness and robustness of RPIPCM. In conclusion, RPIPCM of deep network model based on sequence feature encoding can automatically mine the hidden feature information of the sequence in the lncRNA-protein interaction without relying on external features or prior biomedical knowledge, and its low cost and high efficiency can provide a reference for biomedical researchers.
Collapse
Affiliation(s)
- Lejun Gong
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
| | - Jingmei Chen
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xiong Cui
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Yang Liu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| |
Collapse
|
37
|
Houssein EH, Oliva D, Samee NA, Mahmoud NF, Emam MM. Liver Cancer Algorithm: A novel bio-inspired optimizer. Comput Biol Med 2023; 165:107389. [PMID: 37678138 DOI: 10.1016/j.compbiomed.2023.107389] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/04/2023] [Accepted: 08/25/2023] [Indexed: 09/09/2023]
Abstract
This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.
Collapse
Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Diego Oliva
- Depto. Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico.
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Noha F Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| |
Collapse
|
38
|
Dehghani M, Trojovská E, Trojovský P, Malik OP. OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics (Basel) 2023; 8:468. [PMID: 37887599 PMCID: PMC10604662 DOI: 10.3390/biomimetics8060468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/23/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while preventing the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO's performance in optimization is evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved using the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO in six engineering problems shows the effectiveness of the proposed approach in solving real-world optimization applications.
Collapse
Affiliation(s)
- Mohammad Dehghani
- Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech Republic; (E.T.); (P.T.)
| | - Eva Trojovská
- Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech Republic; (E.T.); (P.T.)
| | - Pavel Trojovský
- Department of Mathematics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech Republic; (E.T.); (P.T.)
| | - Om Parkash Malik
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;
| |
Collapse
|
39
|
Kumar Sahoo S, Houssein EH, Premkumar M, Kumar Saha A, Emam MM. Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation. EXPERT SYSTEMS WITH APPLICATIONS 2023; 227:120367. [PMID: 37193000 PMCID: PMC10163947 DOI: 10.1016/j.eswa.2023.120367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/15/2023] [Accepted: 05/01/2023] [Indexed: 05/18/2023]
Abstract
The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.
Collapse
Affiliation(s)
- Saroj Kumar Sahoo
- Department of Mathematics, National Institute of Technology Agartala, Tripura 799046, India
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - M Premkumar
- Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology Agartala, Tripura 799046, India
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt
| |
Collapse
|
40
|
Alsahafi YS, Elshora DS, Mohamed ER, Hosny KM. Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm. Diagnostics (Basel) 2023; 13:2958. [PMID: 37761325 PMCID: PMC10529071 DOI: 10.3390/diagnostics13182958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/06/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Skin Cancer (SC) is among the most hazardous due to its high mortality rate. Therefore, early detection of this disease would be very helpful in the treatment process. Multilevel Thresholding (MLT) is widely used for extracting regions of interest from medical images. Therefore, this paper utilizes the recent Coronavirus Disease Optimization Algorithm (COVIDOA) to address the MLT issue of SC images utilizing the hybridization of Otsu, Kapur, and Tsallis as fitness functions. Various SC images are utilized to validate the performance of the proposed algorithm. The proposed algorithm is compared to the following five meta-heuristic algorithms: Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Reptile Search Algorithm (RSA), Flower Pollination Algorithm (FPA), Seagull Optimization Algorithm (SOA), and Artificial Gorilla Troops Optimizer (GTO) to prove its superiority. The performance of all algorithms is evaluated using a variety of measures, such as Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), Feature Similarity Index Metric (FSIM), and Normalized Correlation Coefficient (NCC). The results of the experiments prove that the proposed algorithm surpasses several competing algorithms in terms of MSE, PSNR, FSIM, and NCC segmentation metrics and successfully solves the segmentation issue.
Collapse
Affiliation(s)
- Yousef S. Alsahafi
- Department of Information Technology, Khulis College, University of Jeddah, Jeddah 23890, Saudi Arabia;
| | - Doaa S. Elshora
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt; (D.S.E.); (E.R.M.)
| | - Ehab R. Mohamed
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt; (D.S.E.); (E.R.M.)
| | - Khalid M. Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt; (D.S.E.); (E.R.M.)
| |
Collapse
|
41
|
Liu S, Jin H, Di Y. A strategy for predicting waste production and planning recycling paths in e-logistics based on improved EMD-LSTM. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17569-17588. [PMID: 37920066 DOI: 10.3934/mbe.2023780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
With the rapid development of e-commerce, express delivery has been chosen and accepted by consumers, and a large number of express packages have resulted in serious waste of resources and environmental pollution. Because of the irregularity of online goods purchases by users in real life, logistics parks are unable to accurately judge the recycling needs of various regions. In order to solve this problem, we propose an improved empirical mode decomposition (IEMD) algorithm combined with a long-short-term memory (LSTM) network to deal with the addresses and categories in logistics data, analyze the distribution of recyclable logistics waste in the logistics park service area and in the express recycling station within the logistics park, judge the value of recyclable logistics waste, optimize the best path for recycling vehicles and improve the success rate of logistics waste recycling. In order to better research and verify the IEMD-LSTM prediction model, we model and simulate the algorithm behavior of the express waste packaging recycling prediction model system, and compare it with other classification methods through specific logistics data experiments. The prediction accuracy, stability and advantages of the four algorithms are analyzed and compared, and the application reliability of the algorithm proposed in this paper to the logistics waste recycling process is verified. The application in the actual express logistics packaging recycling case shows the feasibility and effectiveness of the waste recycling scheme proposed in this paper.
Collapse
Affiliation(s)
- Shujuan Liu
- School of Logistics, Liaoning Vocational University of Technology, Jinzhou 121007, China
| | - Hui Jin
- School of Logistics, Liaoning Vocational University of Technology, Jinzhou 121007, China
| | - Yanbiao Di
- School of Logistics, Liaoning Vocational University of Technology, Jinzhou 121007, China
| |
Collapse
|
42
|
Zhong M, Wen J, Ma J, Cui H, Zhang Q, Parizi MK. A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study. Comput Biol Med 2023; 164:107212. [PMID: 37478712 DOI: 10.1016/j.compbiomed.2023.107212] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 06/18/2023] [Accepted: 06/25/2023] [Indexed: 07/23/2023]
Abstract
The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.
Collapse
Affiliation(s)
- Mingyang Zhong
- College of Artificial Intelligence, Southwest University, 400715, China.
| | - Jiahui Wen
- Defense Innovation Institute, 100085, China.
| | - Jingwei Ma
- School of Information Science and Engineering, Shandong Normal University, 250399, China.
| | - Hao Cui
- College of Artificial Intelligence, Southwest University, 400715, China.
| | - Qiuling Zhang
- College of Artificial Intelligence, Southwest University, 400715, China.
| | - Morteza Karimzadeh Parizi
- Department of Computer Engineering,Faculty of Shahid Chamran, Kerman Branch,Technical and Vocational University (TVU), Kerman, Iran.
| |
Collapse
|
43
|
Chen H, Wang Z, Jia H, Zhou X, Abualigah L. Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation. Biomimetics (Basel) 2023; 8:396. [PMID: 37754147 PMCID: PMC10526150 DOI: 10.3390/biomimetics8050396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult to find the optimal position in complex functions. The arithmetic optimization algorithm utilizes multiplication and division operators for position updates, which have strong randomness and good convergence ability. For the above, this paper integrates the two algorithms and adds a random central solution strategy, a mutation strategy, and a restart strategy. A hybrid slime mold and arithmetic optimization algorithm with random center learning and restart mutation (RCLSMAOA) is proposed. The improved algorithm retains the position update formula of the slime mold algorithm in the global exploration section. It replaces the convergence stage of the slime mold algorithm with the multiplication and division algorithm in the local exploitation stage. At the same time, the stochastic center learning strategy is adopted to improve the global search efficiency and the diversity of the algorithm population. In addition, the restart strategy and mutation strategy are also used to improve the convergence accuracy of the algorithm and enhance the later optimization ability. In comparison experiments, different kinds of test functions are used to test the specific performance of the improvement algorithm. We determine the final performance of the algorithm by analyzing experimental data and convergence images, using the Wilcoxon rank sum test and Friedman test. The experimental results show that the improvement algorithm, which combines the slime mold algorithm and arithmetic optimization algorithm, is effective. Finally, the specific performance of the improvement algorithm on practical engineering problems was evaluated.
Collapse
Affiliation(s)
- Hongmin Chen
- Department of Information Engineering, Sanming University, Sanming 365004, China; (H.C.); (Z.W.); (X.Z.)
| | - Zhuo Wang
- Department of Information Engineering, Sanming University, Sanming 365004, China; (H.C.); (Z.W.); (X.Z.)
| | - Heming Jia
- Department of Information Engineering, Sanming University, Sanming 365004, China; (H.C.); (Z.W.); (X.Z.)
| | - Xindong Zhou
- Department of Information Engineering, Sanming University, Sanming 365004, China; (H.C.); (Z.W.); (X.Z.)
| | - Laith Abualigah
- Prince Hussein Bin Abdullah College for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan;
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia
| |
Collapse
|
44
|
Xiong X, Li S, Wu F. An Enhanced Neural Network Algorithm with Quasi-Oppositional-Based and Chaotic Sine-Cosine Learning Strategies. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1255. [PMID: 37761554 PMCID: PMC10528600 DOI: 10.3390/e25091255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Global optimization problems have been a research topic of great interest in various engineering applications among which neural network algorithm (NNA) is one of the most widely used methods. However, it is inevitable for neural network algorithms to plunge into poor local optima and convergence when tackling complex optimization problems. To overcome these problems, an improved neural network algorithm with quasi-oppositional-based and chaotic sine-cosine learning strategies is proposed, that speeds up convergence and avoids trapping in a local optimum. Firstly, quasi-oppositional-based learning facilitated the exploration and exploitation of the search space by the improved algorithm. Meanwhile, a new logistic chaotic sine-cosine learning strategy by integrating the logistic chaotic mapping and sine-cosine strategy enhances the ability that jumps out of the local optimum. Moreover, a dynamic tuning factor of piecewise linear chaotic mapping is utilized for the adjustment of the exploration space to improve the convergence performance. Finally, the validity and applicability of the proposed improved algorithm are evaluated by the challenging CEC 2017 function and three engineering optimization problems. The experimental comparative results of average, standard deviation, and Wilcoxon rank-sum tests reveal that the presented algorithm has excellent global optimality and convergence speed for most functions and engineering problems.
Collapse
Affiliation(s)
- Xuan Xiong
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China;
| | - Shaobo Li
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Fengbin Wu
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| |
Collapse
|
45
|
Montazeri Z, Niknam T, Aghaei J, Malik OP, Dehghani M, Dhiman G. Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience. Biomimetics (Basel) 2023; 8:386. [PMID: 37754137 PMCID: PMC10526449 DOI: 10.3390/biomimetics8050386] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/14/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
In this research article, we uphold the principles of the No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration and exploitation, drawing inspiration from the strategic dynamics and player conduct observed in the sport of golf. Through comprehensive assessments encompassing fifty-two objective functions and four real-world engineering applications, the efficacy of the GOA is rigorously examined. The results of the optimization process reveal GOA's exceptional proficiency in both exploration and exploitation strategies, effectively striking a harmonious equilibrium between the two. Comparative analyses against ten competing algorithms demonstrate a clear and statistically significant superiority of the GOA across a spectrum of performance metrics. Furthermore, the successful application of the GOA to the intricate energy commitment problem, considering network resilience, underscores its prowess in addressing complex engineering challenges. For the convenience of the research community, we provide the MATLAB implementation codes for the proposed GOA methodology, ensuring accessibility and facilitating further exploration.
Collapse
Affiliation(s)
- Zeinab Montazeri
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran; (Z.M.); (M.D.)
| | - Taher Niknam
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran; (Z.M.); (M.D.)
| | - Jamshid Aghaei
- School of Engineering & Technology, Central Queensland University, Rockhampton 4701, Australia;
| | - Om Parkash Malik
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Mohammad Dehghani
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran; (Z.M.); (M.D.)
| | - Gaurav Dhiman
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon;
- Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India
- Division of Research and Development, Lovely Professional University, Phagwara 144411, India
| |
Collapse
|
46
|
Zhao Y, Huang C, Zhang M, Cui Y. AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems. Biomimetics (Basel) 2023; 8:381. [PMID: 37622986 PMCID: PMC10452254 DOI: 10.3390/biomimetics8040381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 08/26/2023] Open
Abstract
The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes a metaheuristic algorithm for continuous and constrained global optimization problems, which combines the MOA, the Aquila Optimizer (AO), and the opposition-based learning (OBL) strategy, called AOBLMOA, to overcome the shortcomings of the MOA. The proposed algorithm first fuses the high soar with vertical stoop method and the low flight with slow descent attack method in the AO into the position movement process of the male mayfly population in the MOA. Then, it incorporates the contour flight with short glide attack and the walk and grab prey methods in the AO into the positional movement of female mayfly populations in the MOA. Finally, it replaces the gene mutation behavior of offspring mayfly populations in the MOA with the OBL strategy. To verify the optimization ability of the new algorithm, we conduct three sets of experiments. In the first experiment, we apply AOBLMOA to 19 benchmark functions to test whether it is the optimal strategy among multiple combined strategies. In the second experiment, we test AOBLMOA by using 30 CEC2017 numerical optimization problems and compare it with state-of-the-art metaheuristic algorithms. In the third experiment, 10 CEC2020 real-world constrained optimization problems are used to demonstrate the applicability of AOBLMOA to engineering design problems. The experimental results show that the proposed AOBLMOA is effective and superior and is feasible in numerical optimization problems and engineering design problems.
Collapse
Affiliation(s)
- Yanpu Zhao
- School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China; (Y.Z.); (Y.C.)
| | - Changsheng Huang
- School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China; (Y.Z.); (Y.C.)
| | | | - Yang Cui
- School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China; (Y.Z.); (Y.C.)
| |
Collapse
|
47
|
Du J, Hou J, Wang H, Chen Z. Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:16304-16329. [PMID: 37920014 DOI: 10.3934/mbe.2023728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
To address the issues of unstable, non-uniform and inefficient motion trajectories in traditional manipulator systems, this paper proposes an improved whale optimization algorithm for time-optimal trajectory planning. First, an inertia weight factor is introduced into the surrounding prey and bubble-net attack formulas of the whale optimization algorithm. The value is controlled using reinforcement learning techniques to enhance the global search capability of the algorithm. Additionally, the variable neighborhood search algorithm is incorporated to improve the local optimization capability. The proposed whale optimization algorithm is compared with several commonly used optimization algorithms, demonstrating its superior performance. Finally, the proposed whale optimization algorithm is employed for trajectory planning and is shown to be able to produce smooth and continuous manipulation trajectories and achieve higher work efficiency.
Collapse
Affiliation(s)
- Juan Du
- School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, China
| | - Jie Hou
- School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, China
| | - Heyang Wang
- School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, China
| | - Zhi Chen
- School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, China
| |
Collapse
|
48
|
Abualigah L, Diabat A, Svetinovic D, Elaziz MA. Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems. JOURNAL OF INTELLIGENT MANUFACTURING 2023; 34:2693-2728. [DOI: 10.1007/s10845-022-01921-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/31/2022] [Indexed: 09/02/2023]
|
49
|
Kannan R, Rajasekaran S, Stallon SD, Anand R. Improved indirect instantaneous torque control based torque sharing function approach of SRM drives in EVs using hybrid technique. ISA TRANSACTIONS 2023; 139:322-336. [PMID: 37147220 DOI: 10.1016/j.isatra.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 05/07/2023]
Abstract
This manuscript proposes an improved indirect instantaneous torque control (IITC) based torque sharing function (TSF) method of switched reluctance motor (SRM) drives in electric vehicles (EVs) using a hybrid system. The proposed hybrid techniques are joint performance of both Reptile Search Algorithm (RSA) and Honey Badger Algorithm (HBA), hence it is named as Enhanced RSA (ERSA) method. Here, an IITC method of SRMs for EVs is utilized. It achieves the requirements of the vehicle, like minimum torque ripple, improved speed range, high effectiveness, and maximal torque per ampere (MTPA). To precisely specify the switched reluctance motor and its magnetic features are measured by the proposed method. The modified Torque sharing function compensates the torque error along with incoming phase, which contains the minimal rate of change of flux linkage. Finally, the ERSA method is implemented to define the best control parameters. Then, the proposed ERSA system is performed on the MATLAB platform and the performance is compared to different existing systems. The MSE for case 1 and case 2 using proposed system attains 0.01093 and 0.01095. The voltage deviation for case 1 and case 2 using proposed system reaches 5 and 5. The power factor for case 1 and case 2 reaches a value of 50 and 40 using the proposed system.
Collapse
Affiliation(s)
- R Kannan
- Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
| | - S Rajasekaran
- Department of Electrical and Electronics Engineering, KSR Institute for Engineering and Technology, Tiruchengode, Tamil Nadu, India
| | - S Daison Stallon
- Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - R Anand
- Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
| |
Collapse
|
50
|
Rezaei F, Safavi HR, Abd Elaziz M, Mirjalili S. GMO: geometric mean optimizer for solving engineering problems. Soft comput 2023; 27:10571-10606. [DOI: 10.1007/s00500-023-08202-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 09/01/2023]
|