An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization
This work presents the Whale migrating Algorithm (WMA), an innovative bio-inspired metaheuristic optimization method based on the collaborative migrating behavior of humpback whales. In contrast to conventional methods, WMA integrates leader-follower dynamics with adaptive migratory tactics to balan...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003019 |
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| author | Mojtaba Ghasemi Mohamed Deriche Pavel Trojovský Zulkefli Mansor Mohsen Zare Eva Trojovská Laith Abualigah Absalom E. Ezugwu Soleiman kadkhoda Mohammadi |
| author_facet | Mojtaba Ghasemi Mohamed Deriche Pavel Trojovský Zulkefli Mansor Mohsen Zare Eva Trojovská Laith Abualigah Absalom E. Ezugwu Soleiman kadkhoda Mohammadi |
| author_sort | Mojtaba Ghasemi |
| collection | DOAJ |
| description | This work presents the Whale migrating Algorithm (WMA), an innovative bio-inspired metaheuristic optimization method based on the collaborative migrating behavior of humpback whales. In contrast to conventional methods, WMA integrates leader-follower dynamics with adaptive migratory tactics to balance exploration and exploitation, improving its capacity to evade local optima and converge effectively. The performance of the proposed algorithm was meticulously assessed using the CEC-2005, CEC-2014, and CEC-2017 optimization problems and some restricted engineering problems, exhibiting enhanced accuracy, robustness, and convergence velocity relative to leading optimization techniques, such as PSO, WOA, and GWO. These findings confirm WMA is an effective instrument for addressing intricate optimization challenges across several domains. The source code of the WMA is publicly available at https://www.optim-app.com/projects/wma. |
| format | Article |
| id | doaj-art-13b3f292559b401bb09ac09f9b0fec91 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-13b3f292559b401bb09ac09f9b0fec912025-02-11T04:35:23ZengElsevierResults in Engineering2590-12302025-03-0125104215An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimizationMojtaba Ghasemi0Mohamed Deriche1Pavel Trojovský2Zulkefli Mansor3Mohsen Zare4Eva Trojovská5Laith Abualigah6Absalom E. Ezugwu7Soleiman kadkhoda Mohammadi8Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, IranArtificial Intelligence Research Centre, Ajman University, Ajman, UAEDepartment of Mathematics, Faculty of Science, University of Hradec Králové, Czech Republic; Corresponding authors.Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, MalaysiaDepartment of Electrical Engineering, Faculty of Engineering, Jahrom University, Jahrom, Fars, IranDepartment of Mathematics, Faculty of Science, University of Hradec Králové, Czech RepublicComputer Science Department, Al al-Bayt University, Mafraq 25113, JordanUnit for Data Science and Computing, North-West University, 11 Hofman Street, Potchefstroom 2520, South Africa; Corresponding authors.Department of Electrical Engineering, Boukan Branch, Islamic Azad University, Boukan, IranThis work presents the Whale migrating Algorithm (WMA), an innovative bio-inspired metaheuristic optimization method based on the collaborative migrating behavior of humpback whales. In contrast to conventional methods, WMA integrates leader-follower dynamics with adaptive migratory tactics to balance exploration and exploitation, improving its capacity to evade local optima and converge effectively. The performance of the proposed algorithm was meticulously assessed using the CEC-2005, CEC-2014, and CEC-2017 optimization problems and some restricted engineering problems, exhibiting enhanced accuracy, robustness, and convergence velocity relative to leading optimization techniques, such as PSO, WOA, and GWO. These findings confirm WMA is an effective instrument for addressing intricate optimization challenges across several domains. The source code of the WMA is publicly available at https://www.optim-app.com/projects/wma.http://www.sciencedirect.com/science/article/pii/S2590123025003019Animal intelligenceGlobal optimizationEngineering optimizationBio-inspired metaheuristicsWhale Migration Algorithm |
| spellingShingle | Mojtaba Ghasemi Mohamed Deriche Pavel Trojovský Zulkefli Mansor Mohsen Zare Eva Trojovská Laith Abualigah Absalom E. Ezugwu Soleiman kadkhoda Mohammadi An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization Results in Engineering Animal intelligence Global optimization Engineering optimization Bio-inspired metaheuristics Whale Migration Algorithm |
| title | An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization |
| title_full | An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization |
| title_fullStr | An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization |
| title_full_unstemmed | An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization |
| title_short | An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization |
| title_sort | efficient bio inspired algorithm based on humpback whale migration for constrained engineering optimization |
| topic | Animal intelligence Global optimization Engineering optimization Bio-inspired metaheuristics Whale Migration Algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025003019 |
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