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Zhu W, Fang L, Ye X, Medani M, Escorcia-Gutierrez J. IDRM: Brain tumor image segmentation with boosted RIME optimization. Comput Biol Med 2023; 166:107551. [PMID: 37832284 DOI: 10.1016/j.compbiomed.2023.107551] [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: 06/22/2023] [Revised: 09/13/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023]
Abstract
Timely diagnosis of medical conditions can significantly mitigate the risks they pose to human life. Consequently, there is an urgent demand for an effective auxiliary model that assists physicians in accurately diagnosing medical conditions based on imaging data. While multi-threshold image segmentation models have garnered considerable attention due to their simplicity and ease of implementation, the selection of threshold combinations greatly influences the segmentation performance. Traditional optimization algorithms often require substantial time to address multi-threshold image segmentation problems, and their segmentation accuracy is frequently unsatisfactory. As a result, metaheuristic algorithms have been employed in this domain. However, several algorithms suffer from drawbacks such as premature convergence and inadequate exploration of the solution space when it comes to threshold selection. For instance, the recently proposed optimization algorithm RIME, inspired by the physical phenomenon of rime-ice, falls short in terms of avoiding local optima and fully exploring the solution space. Therefore, this study introduces an enhanced version of RIME, called IDRM, which incorporates an interactive mechanism and Gaussian diffusion strategy. The interactive mechanism facilitates information exchange among agents, enabling them to evolve towards more promising directions and increasing the likelihood of discovering the optimal solution. Additionally, the Gaussian diffusion strategy enhances the agents' local exploration capabilities and expands their search within the solution space, effectively preventing them from becoming trapped in local optima. Experimental results on 30 benchmark test functions demonstrate that IDRM exhibits favorable optimization performance across various optimization functions, showcasing its robustness and convergence properties. Furthermore, the algorithm is applied to select threshold combinations for brain tumor image segmentation, and the results are evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The overall findings consistently highlight the exceptional performance of this approach, further validating the effectiveness of IDRM in addressing image segmentation problems.
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Affiliation(s)
- Wei Zhu
- School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
| | - Liming Fang
- School of Humanities and Communication, Zhejiang Gongshang University, Hangzhou, 310000, China.
| | - Xia Ye
- School of the 1st Clinical Medical Sciences(School of Information and Engineering), Wenzhou Medical University, Wenzhou, 325000, China.
| | - Mohamed Medani
- Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Muhayil Aseer, 62529, Saudi Arabia.
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia.
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2
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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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Hou L, Li R, Mafarja M, Heidari AA, Liu L, Jin C, Zhou S, Chen H, Cai Z, Li C. Image segmentation of Intracerebral hemorrhage patients based on enhanced hunger Games search Optimizer. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Sadeghi H, Ajoudanian S. Optimized Feature Selection in Software Product Lines using Discrete Bat Algorithm. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2022. [DOI: 10.1142/s1469026822500031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population.
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Affiliation(s)
- Hajar Sadeghi
- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Shohreh Ajoudanian
- Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
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5
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Ghaedi A, Bardsiri AK, Shahbazzadeh MJ. Cat hunting optimization algorithm: a novel optimization algorithm. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00668-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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6
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Random offset minimization in low frequency front-end amplifiers using swarm intelligence based techniques. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00495-5] [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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Abstract
One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.
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Al-qaness MAA, Ewees AA, Abd Elaziz M. Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems. Soft comput 2021. [DOI: 10.1007/s00500-021-05889-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Ehteram M, Ahmed AN, Latif SD, Huang YF, Alizamir M, Kisi O, Mert C, El-Shafie A. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:1596-1611. [PMID: 32851519 DOI: 10.1007/s11356-020-10421-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 08/06/2020] [Indexed: 06/11/2023]
Abstract
There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.
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Affiliation(s)
- Mohammad Ehteram
- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
| | - Ali Najah Ahmed
- Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Sarmad Dashti Latif
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia
| | - Yuk Feng Huang
- Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia.
| | - Meysam Alizamir
- Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
| | - Ozgur Kisi
- Department of Civil Engineering, Ilia State University, Tbilisi, Georgia
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
| | - Cihan Mert
- Faculty of Computer Technologies and Engineering, International Black Sea University, Tbilisi, Georgia
| | - Ahmed El-Shafie
- Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603 Kuala Lumpur, Malaysia
- National Water Center, United Arab Emirates University (UAEU), 15551, Al Ain, United Arab Emirates
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El-Kenawy ESM, Ibrahim A, Mirjalili S, Eid MM, Hussein SE. Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:179317-179335. [PMID: 34976558 PMCID: PMC8545288 DOI: 10.1109/access.2020.3028012] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 09/27/2020] [Indexed: 05/07/2023]
Abstract
Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.
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Affiliation(s)
- El-Sayed M. El-Kenawy
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and OptimizationTorrens University AustraliaFortitude ValleyQLD4006Australia
- Yonsei Frontier Laboratory (YFL)Yonsei UniversitySeoul03722South Korea
| | - Marwa Metwally Eid
- Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET)Mansoura35111Egypt
| | - Sherif E. Hussein
- Computer Engineering and Control Systems DepartmentFaculty of EngineeringMansoura UniversityMansoura35516Egypt
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Li J, Lu X, Zhang Y, Cheng F, Li Y, Wen X, Yang S. Transmittance Tunable Smart Window Based on Magnetically Responsive 1D Nanochains. ACS APPLIED MATERIALS & INTERFACES 2020; 12:31637-31644. [PMID: 32559372 DOI: 10.1021/acsami.0c08402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Smart optical materials are drawing more and more attention because of their wide application in energy conservation, wearable sensors, optical tuning, and medical devices. However, current smart optical materials, including electroresponsive, thermoresponsive, and mechanoresponsive materials, are greatly restricted in practical applications because of their long response time, complicated preparation, and high cost. This study develops a novel, magnetically tunable, smart optical material with swift and high-contrast optical switching based on one-dimensional (1D) Fe3O4@SiO2 nanochains (NCs), which have the large shape anisotropy of the 1D structure and the superparamagnetic properties of Fe3O4 particles. The material exhibited a clear transparent state when NCs were arranged parallel to the viewing direction under an applied magnetic field, whereas it showed good shielding effect when the NCs were randomly oriented upon removal of the field. The light transmittance could be dynamically adjusted over the wide range of 20-80% through a small applied magnetic field of 50-100 Oe, which is superior to most of the currently reported systems. This swift, sensitive, and reversible response is attributed to the good responsivity of magnetic NCs. Also, an effective model was proposed to explain the transmittance modulation scheme and forecast its optical potential. The large tunable range and the low triggered field make Fe3O4@SiO2 NCs an advantageous candidate for application in smart windows, optical switchers, and other fields.
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Affiliation(s)
- Jianing Li
- Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Xuegang Lu
- Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Yin Zhang
- Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Fei Cheng
- Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Yanlin Li
- Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Xiaoxiang Wen
- Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China
| | - Sen Yang
- Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Science, Xi'an Jiaotong University, Xi'an 710049, P. R. China
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Akdag O, Ates A, Yeroglu C. Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05073-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Salgotra R, Singh U, Saha S. On Some Improved Versions of Whale Optimization Algorithm. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-04016-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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