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Kumar D, Mandal N, Kumar Y. Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling. BIG DATA 2024; 12:110-126. [PMID: 36867158 DOI: 10.1089/big.2022.0095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.
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Affiliation(s)
- Dipesh Kumar
- Department of Electronics Engineering, Indian Institute of Technology (ISM), Dhanbad, India
| | - Nirupama Mandal
- Department of Electronics Engineering, Indian Institute of Technology (ISM), Dhanbad, India
| | - Yugal Kumar
- Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan, India
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2
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Xie S, Lei L, Sun J, Xu J. [Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:1-8. [PMID: 38403598 PMCID: PMC10894732 DOI: 10.7507/1001-5515.202303010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.
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Affiliation(s)
- Songyun Xie
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China
| | - Lingjun Lei
- Medical Research Institute, Northwestern Polytechnical University, Xi'an 710129, P. R. China
| | - Jiang Sun
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China
| | - Jian Xu
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P. R. China
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3
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Pham VHS, Nguyen VN, Nguyen Dang NT. Hybrid whale optimization algorithm for enhanced routing of limited capacity vehicles in supply chain management. Sci Rep 2024; 14:793. [PMID: 38191905 PMCID: PMC10774344 DOI: 10.1038/s41598-024-51359-2] [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: 06/12/2023] [Accepted: 01/03/2024] [Indexed: 01/10/2024] Open
Abstract
The present study focuses on the problem of vehicle routing with limited capacity, with the objective of minimizing the transportation distance required to serve h clients with predetermined locations and needs. The aim is to create k trips that cover the shortest possible distance. To achieve this goal, a hybrid whale optimization algorithm (hGWOA) is proposed, which combines the whale optimization algorithm (WOA) with the grey wolf optimizer (GWO). The proposed hybrid model is comprised of two main steps. First step, the GWO's hunting mechanism is integrated transitioning to the utilization phase of WOA, and a newly devised state is introduced that is linked to GWO. In the second step, a novel technique is incorporated into the exploration mission phase to enhance the resolve after per iteration. The algorithm's performance is assessed and compared with other modern algorithms, including the GWO, WOA, ant lion optimizer (ALO), and dragonfly algorithm (DA) using 23 benchmark test functions and CEC2017 benchmark test function. The results indicate that the hybrid hGWOA method outperforms other algorithms in terms of delivery distance optimization for scenarios involving scale and complexity. These findings are corroborated through case studies related to cement delivery and a real-world scenario in Viet Nam.
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Affiliation(s)
- Vu Hong Son Pham
- Department of Construction Engineering and Management, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam
| | - Van Nam Nguyen
- Department of Construction Engineering and Management, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam.
| | - Nghiep Trinh Nguyen Dang
- Department of Construction Engineering and Management, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM), Ho Chi Minh City, Vietnam
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4
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Magdy O, Abd Elaziz M, Elgarayhi A, Ewees AA, Sallah M. Bone metastasis detection method based on improving golden jackal optimization using whale optimization algorithm. Sci Rep 2023; 13:15019. [PMID: 37699992 PMCID: PMC10497577 DOI: 10.1038/s41598-023-41733-x] [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: 04/10/2023] [Accepted: 08/30/2023] [Indexed: 09/14/2023] Open
Abstract
This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique's performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes.
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Affiliation(s)
- Omnia Magdy
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt.
- Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt.
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE.
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
- MEU Research Unit, Middle East University, Amman, Jordan.
| | - Ahmed Elgarayhi
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta, 34517, Egypt.
| | - Mohammed Sallah
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
- Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha , 61922, Saudi Arabia
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5
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Xu Y, Zhang B, Zhang Y. Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor. Biomimetics (Basel) 2023; 8:354. [PMID: 37622959 PMCID: PMC10452118 DOI: 10.3390/biomimetics8040354] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/26/2023] Open
Abstract
The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed.
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Affiliation(s)
| | | | - Yi Zhang
- College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130119, China; (Y.X.); (B.Z.)
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Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh Z, Mirjalili S. A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1-47. [PMID: 37359740 PMCID: PMC10220350 DOI: 10.1007/s11831-023-09928-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.
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Affiliation(s)
- Mohammad H. Nadimi-Shahraki
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Hoda Zamani
- Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, 8514143131 Iran
| | - Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, 4006 Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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7
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Shankar DD, Azhakath AS. Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization. Sci Rep 2023; 13:2359. [PMID: 36759703 PMCID: PMC9911593 DOI: 10.1038/s41598-023-29453-8] [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: 08/19/2022] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication. With the advent of technology, the data has become susceptible to mismanagement and exploitation. This led to the emergence of Internet Security frameworks like Information hiding and detection. Examples of domains of Information hiding and detection are Steganography and steganalysis respectively. This work focus on addressing possible security breaches using Internet security framework like Information hiding and techniques to identify the presence of a breach. The work involves the use of Blind steganalysis technique with the concept of Machine Learning incorporated into it. The work is done using the Joint Photographic Expert Group (JPEG) format because of its wide use for transmission over the Internet. Stego (embedded) images are created for evaluation by randomly embedding a text message into the image. The concept of calibration is used to retrieve an estimate of the cover (clean) image for analysis. The embedding is done with four different steganographic schemes in both spatial and transform domain namely LSB Matching and LSB Replacement, Pixel Value Differencing and F5. After the embedding of data with random percentages, the first order, the second order, the extended Discrete Cosine Transform (DCT) and Markov features are extracted for steganalysis.The above features are a combination of interblock and intra block dependencies. They had been considered in this paper to eliminate the drawback of each one of them, if considered separately. Dimensionality reduction is applied to the features using Principal Component Analysis (PCA). Block based technique had been used in the images for better accuracy of results. The technique of machine learning is added by using classifiers to differentiate the stego image from a cover image. A comparative study had been during with the classifier names Support Vector Machine and its evolutionary counterpart using Particle Swarm Optimization. The idea of cross validation had also been used in this work for better accuracy of results. Further parameters used in the process are the four different types of sampling namely linear, shuffled, stratified and automatic and the six different kernels used in classification specifically dot, multi-quadratic, epanechnikov, radial and ANOVA to identify what combination would yield a better result.
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Affiliation(s)
- Deepa D. Shankar
- grid.444459.c0000 0004 1762 9315Abu Dhabi University, Abu Dhabi, United Arab Emirates
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8
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Kaur S, Kumar Y, Koul A, Kumar Kamboj S. A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:1863-1895. [PMID: 36465712 PMCID: PMC9702927 DOI: 10.1007/s11831-022-09853-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.
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Affiliation(s)
- Sukhpreet Kaur
- Department of Computer Science and Engineering, CGC Landran, Mohali, India
| | - Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
| | - Apeksha Koul
- Department of Computer Science and Engineering, Punjabi University, Patiala, India
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9
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Riyahi M, Rafsanjani MK, Gupta BB, Alhalabi W. Multiobjective whale optimization algorithm‐based feature selection for intelligent systems. INT J INTELL SYST 2022. [DOI: 10.1002/int.22979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Milad Riyahi
- Department of Computer Science, Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran
| | - Marjan K. Rafsanjani
- Department of Computer Science, Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran
| | - Brij B. Gupta
- Department of Computer Science and Information Engineering Asia University Taichung Taiwan
- Lebanese American University Beirut Lebanon
- Center for Interdisciplinary Research UPES Dehradun India
- Research and Innovation Department Skyline University College Sharjah United Arab Emirates
| | - Wadee Alhalabi
- Department of Electrical and Computer Engineering University of Miami Coral Gables Florida USA
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10
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Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm. ENERGIES 2022. [DOI: 10.3390/en15134571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.
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11
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Joseph SB, Dada EG, Abidemi A, Oyewola DO, Khammas BM. Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems. Heliyon 2022; 8:e09399. [PMID: 35600459 PMCID: PMC9120253 DOI: 10.1016/j.heliyon.2022.e09399] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/16/2022] [Accepted: 05/05/2022] [Indexed: 11/28/2022] Open
Abstract
The simplicity, transparency, reliability, high efficiency and robust nature of PID controllers are some of the reasons for their high popularity and acceptance for control in process industries around the world today. Tuning of PID control parameters has been a field of active research and still is. The primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time. With exception of two popular conventional tuning strategies (Ziegler Nichols closed loop oscillation and Cohen-Coon's process reaction curve) several other methods have been employed for tuning. This work accords a thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms. Methods appraised are categorized into classical and metaheuristic optimization methods for PID parameters tuning purposes. Details of some metaheuristic algorithms, methods of application, equations and implementation flowcharts/algorithms are presented. Some open problems for future research are also presented. The major goal of this work is to proffer a comprehensive reference source for researchers and scholars working on PID controllers.
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Affiliation(s)
- Stephen Bassi Joseph
- Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria
| | - Emmanuel Gbenga Dada
- Department of Mathematical Sciences, Faculty of Science, University of Maiduguri, Maiduguri, Nigeria
| | - Afeez Abidemi
- Department of Mathematical Sciences, Federal University of Technology, Akure, Nigeria
| | - David Opeoluwa Oyewola
- Department of Mathematics and Computer Science, Federal University Kashere, Gombe, Nigeria
| | - Ban Mohammed Khammas
- Department of Computer Networks Engineering, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
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FOX: a FOX-inspired optimization algorithm. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03533-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Identifying the Association of Time-Averaged Serum Albumin Levels with Clinical Factors among Patients on Hemodialysis Using Whale Optimization Algorithm. MATHEMATICS 2022. [DOI: 10.3390/math10071030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered as a surrogate indicator of nutritional status. The whale optimization algorithm-based feature selection (WOFS) model could address the complex association between the clinical factors, and could further combine with regression models for application. The present study aimed to demonstrate an optimal multifactor TSA-associated model, in order to interpret the complex association between TSA and clinical factors among HD patients. A total of 829 HD patients who met the inclusion criteria were selected for analysis. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted, stepwise, and WOFS models. Five features, albumin, age, creatinine, potassium, and HD adequacy index (Kt/V level), were selected from fifteen clinical factors by the WOFS model, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction. The WOFS model yielded the lowest Akaike information criterion (AIC) value, which indicated that the WOFS model could achieve superior performance in the multifactor analysis of TSA for HD patients. In conclusion, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients.
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Ghany KKA, AbdelAziz AM, Soliman THA, Sewisy AAEM. A hybrid modified step Whale Optimization Algorithm with Tabu Search for data clustering. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2020.01.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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A new feature extraction technique based on improved owl search algorithm: a case study in copper electrorefining plant. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06881-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Ferrari ACK, Silva CAGD, Osinski C, Pelacini DAF, Leandro GV, Coelho LDS. Tuning of control parameters of the Whale Optimization Algorithm using fuzzy inference system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-210781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The Whale Optimization Algorithm (WOA) is a recent approach to the swarm intelligence field that can be explored in many global optimization applications. This paper proposes a new mechanism to tune the control parameters that influence the hunting process in the WOA to improve its convergence rate. This schema adjustment is made by a fuzzy inference system that uses the normalized fitness value of each whale and the hunting mechanism control parameters of WOA. The method proposed was tested and compared with the conventional WOA and another version that uses a fuzzy inference system as input information on the ratio of the current iteration number and the maximum number of iterations. For performance analysis of the method proposed, all optimizers were evaluated with twenty-three benchmark optimization functions in the continuous domain. The algorithms were also implemented in the identification process of two real control system that are a boiler system and water supply network. For identification process, it is used the value of MSE (mean squared error) to available each algorithm. The simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional WOA and it is competitive in relation to another fuzzy version adopted in the WOA design.
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Affiliation(s)
| | | | - Cristiano Osinski
- Department of Electrical Engineering, Electrical Engineering Graduate Program, Federal University of Paraná, Curitiba, Brazil
| | | | - Gideon Villar Leandro
- Department of Electrical Engineering, Electrical Engineering Graduate Program, Federal University of Paraná, Curitiba, Brazil
| | - Leandro dos Santos Coelho
- Department of Electrical Engineering, Electrical Engineering Graduate Program, Federal University of Paraná, Curitiba, Brazil
- Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Paraná, Curitiba, Brazil
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17
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Das S, Nayak M, Senapati MR, Majhi S. Improving Time Series Prediction With Feature Selection Using a Velocity-Enhanced Whale Optimization Algorithm. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022. [DOI: 10.4018/ijsir.307104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The nonlinearity and uncertain behavior of many recent financial applications is increasing rapidly. Thus, it is important to resolve the rapid growth of time-variant problems with the help of artificial intelligence methods. In this paper, a hybridized method is used to predict four types of financial datasets: absenteeism at work, blog feedback data, currency exchange rate, and energy consumption. The prediction accuracy is improved with feature selection techniques. During the use of feature selection methods, only related features are carefully chosen and then fed to the neural network algorithm for prediction. In this research, the previous year data is taken for training and recent year data is taken for testing. Finally, the results of the velocity enhanced whale optimization algorithm (VEWOA) is compared with other methods like local linear wavelet neural network (LLWNN) and local linear radial basis functional neural network (LLRBFNN).
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Affiliation(s)
- Soumya Das
- Government College of Engineering, Kalahandi, India
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Liu K, Li W, Ye P, Zhang Z, Ji Q, Wu Z. The Bent-Tube Nozzle Optimization of Force-Spinning With the Gray Wolf Algorithm. Front Bioeng Biotechnol 2021; 9:807287. [PMID: 34976994 PMCID: PMC8714732 DOI: 10.3389/fbioe.2021.807287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/29/2021] [Indexed: 12/02/2022] Open
Abstract
Force-spinning is a popular way to fabricate various fine fibers such as polymer and metal nanofibers, which are being widely employed in medical and industrial manufacture. The spinneret is the key of the device for spinning fibers, and the physical performance and morphology of the spun nanofibers are largely determined by its structure parameters. In this article, the effect of spinneret parameters on the outlet velocity is explored and the spinneret parameters are also optimized to obtain the maximum outlet velocity. The mathematical model of the solution flow in four areas is established at first, and the relationship between outlet velocity and structure parameters is acquired. This model can directly reflect the flow velocity of the solution in each area. Then, the optimal parameters of outlet diameter, bending angle, and curvature radius are obtained combined with the gray wolf algorithm (GWA). It is found that a curved-tube nozzle with a bending angle of 9.1°, nozzle diameter of 0.6 mm, and curvature radius of 10 mm can obtain the maximum outlet velocity and better velocity distribution. Subsequently, the simulation is utilized to analyze and compare the velocity situation of different parameters. Finally, the fiber of 5 wt% PEO solution is manufactured by a straight-tube nozzle and optimized bent-tube nozzle in the laboratory, and the morphology and diameter distribution were observed using a scanning electron microscope (SEM). The results showed that the outlet velocity was dramatically improved after the bent-tube parameters were optimized by GWA, and nanofibers of better surface quality could be obtained using optimized bent-tube nozzles.
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Affiliation(s)
- Kang Liu
- Hubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, China
| | - Wenhui Li
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
| | - Peiyan Ye
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
| | - Zhiming Zhang
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
- *Correspondence: Zhiming Zhang,
| | - Qiaoling Ji
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
| | - Zijun Wu
- School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
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An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism. PLoS One 2021; 16:e0260725. [PMID: 34847188 PMCID: PMC8631685 DOI: 10.1371/journal.pone.0260725] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 11/20/2022] Open
Abstract
Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a new metaheuristic algorithm, an improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism (TAVOA), is proposed. First, a tent chaotic map is introduced for population initialization. Second, the individual's historical optimal position is recorded and applied to individual location updating. Third, a time-varying mechanism is designed to balance the exploration ability and exploitation ability. To verify the effectiveness and efficiency of TAVOA, TAVOA is tested on 23 basic benchmark functions, 28 CEC 2013 benchmark functions and 3 common real-world engineering design problems, and compared with AVOA and 5 other state-of-the-art metaheuristic optimization algorithms. According to the results of the Wilcoxon rank-sum test with 5%, among the 23 basic benchmark functions, the performance of TAVOA has significantly better than that of AVOA on 13 functions. Among the 28 CEC 2013 benchmark functions, the performance of TAVOA on 9 functions is significantly better than AVOA, and on 17 functions is similar to AVOA. Besides, compared with the six metaheuristic optimization algorithms, TAVOA also shows good performance in real-world engineering design problems.
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A novel hybrid firefly–whale optimization algorithm and its application to optimization of MPC parameters. Soft comput 2021. [DOI: 10.1007/s00500-021-06441-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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21
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Raihan MJ, Nahid AA. Malaria cell image classification by explainable artificial intelligence. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00620-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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22
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Rahman CM, Rashid TA, Alsadoon A, Bacanin N, Fattah P, Mirjalili S. A survey on dragonfly algorithm and its applications in engineering. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00659-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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23
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A new evolutionary algorithm: Learner performance based behavior algorithm. EGYPTIAN INFORMATICS JOURNAL 2021. [DOI: 10.1016/j.eij.2020.08.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Ashraf NM, Mostafa RR, Sakr RH, Rashad MZ. Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm. PLoS One 2021; 16:e0252754. [PMID: 34111168 PMCID: PMC8191943 DOI: 10.1371/journal.pone.0252754] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/22/2021] [Indexed: 11/24/2022] Open
Abstract
Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-designed reward function that suites a particular environment without any prior knowledge related to a given environment. The adaptation of hyperparameters has a great impact on the overall learning process and the learning processing times. Hyperparameters should be accurately estimated while training DRL algorithms, which is one of the key challenges that we attempt to address. This paper employs a swarm-based optimization algorithm, namely the Whale Optimization Algorithm (WOA), for optimizing the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve the optimum control strategy in an autonomous driving control problem. DDPG is capable of handling complex environments, which contain continuous spaces for actions. To evaluate the proposed algorithm, the Open Racing Car Simulator (TORCS), a realistic autonomous driving simulation environment, was chosen to its ease of design and implementation. Using TORCS, the DDPG agent with optimized hyperparameters was compared with a DDPG agent with reference hyperparameters. The experimental results showed that the DDPG's hyperparameters optimization leads to maximizing the total rewards, along with testing episodes and maintaining a stable driving policy.
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Affiliation(s)
- Nesma M. Ashraf
- Computer Science Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
| | - Reham R. Mostafa
- Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
| | - Rasha H. Sakr
- Computer Science Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
| | - M. Z. Rashad
- Computer Science Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt
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Munien C, Ezugwu AE. Metaheuristic algorithms for one-dimensional bin-packing problems: A survey of recent advances and applications. JOURNAL OF INTELLIGENT SYSTEMS 2021. [DOI: 10.1515/jisys-2020-0117] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem, which is defined as the placement of a set of different-sized items into identical bins such that the number of containers used is optimally minimized. Besides, different variations of the problem do exist in practice depending on the bins dimension, placement constraints, and priority. More so, there are several important real-world applications of the BPP, especially in cutting industries, transportation, warehousing, and supply chain management. Due to the practical relevance of this problem, researchers are consistently investigating new and improved techniques to solve the problem optimally. Nature-inspired metaheuristics are powerful algorithms that have proven their incredible capability of solving challenging and complex optimization problems, including several variants of BPPs. However, no comprehensive literature review exists on the applications of the metaheuristic approaches to solve the BPPs. Therefore, to fill this gap, this article presents a survey of the recent advances achieved for the one-dimensional BPP, with specific emphasis on population-based metaheuristic algorithms. We believe that this article can serve as a reference guide for researchers to explore and develop more robust state-of-the-art metaheuristics algorithms for solving the emerging variants of the bin-parking problems.
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Affiliation(s)
- Chanaleä Munien
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Private Bag Box X54001 , Durban 4000 , South Africa
| | - Absalom E. Ezugwu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal , Pietermaritzburg , 3201 , South Africa
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Wang C, Li J, Rao H, Chen A, Jiao J, Zou N, Gu L. Multi-objective grasshopper optimization algorithm based on multi-group and co-evolution. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:2527-2561. [PMID: 33892559 DOI: 10.3934/mbe.2021129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The balance between exploration and exploitation is critical to the performance of a Meta-heuristic optimization method. At different stages, a proper tradeoff between exploration and exploitation can drive the search process towards better performance. This paper develops a multi-objective grasshopper optimization algorithm (MOGOA) with a new proposed framework called the Multi-group and Co-evolution Framework which can archive a fine balance between exploration and exploitation. For the purpose, a grouping mechanism and a co-evolution mechanism are designed and integrated into the framework for ameliorating the convergence and the diversity of multi-objective optimization solutions and keeping the exploration and exploitation of swarm intelligence algorithm in balance. The grouping mechanism is employed to improve the diversity of search agents for increasing coverage of search space. The co-evolution mechanism is used to improve the convergence to the true Pareto optimal front by the interaction of search agents. Quantitative and qualitative outcomes prove that the framework prominently ameliorate the convergence accuracy and convergence speed of MOGOA. The performance of the presented algorithm has been benchmarked by several standard test functions, such as CEC2009, ZDT and DTLZ. The diversity and convergence of the obtained multi-objective optimization solutions are quantitatively and qualitatively compared with the original MOGOA by using two performance indicators (GD and IGD). The results on test suits show that the diversity and convergence of the obtained solutions are significantly improved. On several test functions, some statistical indicators are more than doubled. The validity of the results has been verified by the Wilcoxon rank-sum test.
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Affiliation(s)
- Chao Wang
- Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce of the Ministry of Agriculture, Hefei 230036, China
| | - Jian Li
- Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce of the Ministry of Agriculture, Hefei 230036, China
| | - Haidi Rao
- Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce of the Ministry of Agriculture, Hefei 230036, China
| | - Aiwen Chen
- Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce of the Ministry of Agriculture, Hefei 230036, China
| | - Jun Jiao
- Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce of the Ministry of Agriculture, Hefei 230036, China
| | - Nengfeng Zou
- Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce of the Ministry of Agriculture, Hefei 230036, China
| | - Lichuan Gu
- Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce of the Ministry of Agriculture, Hefei 230036, China
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Abbas S, Jalil Z, Javed AR, Batool I, Khan MZ, Noorwali A, Gadekallu TR, Akbar A. BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm. PeerJ Comput Sci 2021; 7:e390. [PMID: 33817036 PMCID: PMC7959601 DOI: 10.7717/peerj-cs.390] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 01/20/2021] [Indexed: 06/12/2023]
Abstract
Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.
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Affiliation(s)
- Shafaq Abbas
- Department of Computer Science, Air University, Islamabad, Pakistan
| | - Zunera Jalil
- Department of Cyber Security, Air University, Islamabad, Pakistan
| | | | - Iqra Batool
- Department of Computer Science, Air University, Islamabad, Pakistan
| | - Mohammad Zubair Khan
- Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
| | | | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology University, Tamil Nadu, India
| | - Aqsa Akbar
- Department of Computer Science, Air University, Islamabad, Pakistan
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Pathan S, Siddalingaswamy PC, Ali T. Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture. Appl Soft Comput 2021; 104:107238. [PMID: 33649705 PMCID: PMC7903912 DOI: 10.1016/j.asoc.2021.107238] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/21/2021] [Accepted: 02/21/2021] [Indexed: 12/13/2022]
Abstract
The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of lesser sensitivity testing kits, and limited availability of healthcare professionals, the pandemic has spread like wildfire across the world. Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. This is where artificial intelligence meets the diagnostic capabilities of experienced clinicians. The objective of the proposed research is to contribute towards fighting the global pandemic by developing an automated image analysis module for identifying covid-19 affected chest X-ray scans by employing an optimized Convolution Neural Network (CNN) model. The aforementioned objective is achieved in the following manner by developing three classification models, (i) ensemble of ResNet 50-Error Correcting Output Code (ECOC) model, (ii) CNN optimized using Grey Wolf Optimizer (GWO) and, (iii) CNN optimized using Whale Optimization + BAT algorithm. The novelty of the proposed method lies in the automatic tuning of hyper parameters considering a hierarchy of MultiLayer Perceptron (MLP), feature extraction, and optimization ensemble. A 100% classification accuracy was obtained in classifying covid-19 images. Classification accuracy of 98.8% and 96% were obtained for dataset 1 and dataset 2 respectively for classification into covid-19, normal, and viral pneumonia cases. The proposed method can be adopted in a clinical setting for assisting radiologists and it can also be employed in remote areas to facilitate the faster screening of affected patients.
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Affiliation(s)
- Sameena Pathan
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - P C Siddalingaswamy
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Tanweer Ali
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
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An Optimized Framework for Energy-Resource Allocation in A Cloud Environment based on the Whale Optimization Algorithm. SENSORS 2021; 21:s21051583. [PMID: 33668282 PMCID: PMC7956425 DOI: 10.3390/s21051583] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/08/2021] [Accepted: 02/19/2021] [Indexed: 11/16/2022]
Abstract
Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.
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A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106903] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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32
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An Improved Whale Optimization Algorithm with Random Evolution and Special Reinforcement Dual-Operation Strategy Collaboration. Symmetry (Basel) 2021. [DOI: 10.3390/sym13020238] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
In view of the slow convergence speed, difficulty of escaping from the local optimum, and difficulty maintaining the stability associated with the basic whale optimization algorithm (WOA), an improved WOA algorithm (REWOA) is proposed based on dual-operation strategy collaboration. Firstly, different evolutionary strategies are integrated into different dimensions of the algorithm structure to improve the convergence accuracy and the randomization operation of the random Gaussian distribution is used to increase the diversity of the population. Secondly, special reinforcements are made to the process involving whales searching for prey to enhance their exclusive exploration or exploitation capabilities, and a new skip step factor is proposed to enhance the optimizer’s ability to escape the local optimum. Finally, an adaptive weight factor is added to improve the stability of the algorithm and maintain a balance between exploration and exploitation. The effectiveness and feasibility of the proposed REWOA are verified with the benchmark functions and different experiments related to the identification of the Hammerstein model.
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Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms. OPTICAL FIBER TECHNOLOGY 2020; 60:102357. [PMCID: PMC7534752 DOI: 10.1016/j.yofte.2020.102357] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 09/07/2020] [Accepted: 09/09/2020] [Indexed: 05/30/2023]
Abstract
In FiWi network placement of multiple ONUs problem has been solved. Grasshopper Optimization and Harris Hawks Optimization Algorithms have been implemented. Harris Hawks optimizer outperformed the other existing strategies of ONUs placement. Optimal values of controlling parameters of aforementioned optimizers have been determined.
FiWi network is a multi-domain network that integrates optical and wireless networks. Hybrid fiber-wireless (FiWi) access network endeavours at consolidating the huge amount of available bandwidth of optical networks and the ubiquity, mobility of wireless access networks with the motive of reducing cost and complexity. This manuscript entails investigations undertaken towards optimal placement of multiple Optical Network Units (ONUs) in FiWi network using two recent optimization algorithms named as Harris Hawks Optimization (HHO) and Grasshopper Optimization algorithms (GOA). The results of the investigations are then benchmarked with respect to the Whale Optimization algorithm (WOA). The outcomes demonstrate the superiority of the proposed HHO algorithm over GOA and WOA algorithms, and return the lowest value of cost function; WOA outperforms GOA in terms of improved convergence rate and time complexity. Additionally, the diversification and intensification features of HHO, GOA and WOA have been compared. In order to benchmark the performance of the HHO and GOA optimizers, a series of convergence curves corresponding to different values of controlling parameters are plotted, and their optimal values determined. The dependence of objective function value upon distribution of users and initial placement of ONUs is also studied; the random placement of users and deterministic placement of ONUs return the lowest value of objective function.
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Mohammed H, Rashid T. A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04823-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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35
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Xiaoxiao X, Bin L, Ramkumar S, Saravanan S, Balaji MSP, Dhanasekaran S, Thimmiaraja J. Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model. Artif Intell Med 2020; 102:101766. [PMID: 31980103 DOI: 10.1016/j.artmed.2019.101766] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/16/2019] [Accepted: 11/18/2019] [Indexed: 12/14/2022]
Abstract
Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.
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Affiliation(s)
- Xu Xiaoxiao
- School of Entrepreneurship, Wuhan University of Technology, Wuhan Hubei Province, 430070, China.
| | - Luo Bin
- School of Foreign Languages, Wuhan Business University, Wuhan, 430056, China
| | - S Ramkumar
- School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India
| | - S Saravanan
- Department of Information Science and Engineering, CMR Institute of Technology, Bangalore, India
| | | | - S Dhanasekaran
- School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India
| | - J Thimmiaraja
- School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India
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Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:4854895. [PMID: 32405296 PMCID: PMC7204373 DOI: 10.1155/2020/4854895] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/15/2019] [Accepted: 12/20/2019] [Indexed: 11/17/2022]
Abstract
This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.
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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation. MATHEMATICS 2019. [DOI: 10.3390/math7121229] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes.
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Dragonfly Algorithm and Its Applications in Applied Science Survey. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:9293617. [PMID: 31885533 PMCID: PMC6925939 DOI: 10.1155/2019/9293617] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 10/24/2019] [Accepted: 11/13/2019] [Indexed: 11/30/2022]
Abstract
One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.
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