Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks
The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into loca...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8130378 |
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| _version_ | 1832549153759035392 |
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| author | GuoChun Wang Wenyong Gui Guoxi Liang Xuehua Zhao Mingjing Wang Majdi Mafarja Hamza Turabieh Junyi Xin Huiling Chen Xinsheng Ma |
| author_facet | GuoChun Wang Wenyong Gui Guoxi Liang Xuehua Zhao Mingjing Wang Majdi Mafarja Hamza Turabieh Junyi Xin Huiling Chen Xinsheng Ma |
| author_sort | GuoChun Wang |
| collection | DOAJ |
| description | The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA further balances the exploration and exploitation phases and makes it easier for the algorithm to get rid of the local optimum. To show the proposed method’s performance, MEWOA is contrasted to other superior algorithms on a series of comprehensive benchmark functions and applied to practical engineering problems. The experimental data reveal that the MEWOA is better than the contrast algorithms in convergence speed and solution quality. Hence, it can be concluded that MEWOA has great potential in global optimization. |
| format | Article |
| id | doaj-art-38d1b29c0e0d49819e2426d843fcc944 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-38d1b29c0e0d49819e2426d843fcc9442025-02-03T06:11:56ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/81303788130378Spiral Motion Enhanced Elite Whale Optimizer for Global TasksGuoChun Wang0Wenyong Gui1Guoxi Liang2Xuehua Zhao3Mingjing Wang4Majdi Mafarja5Hamza Turabieh6Junyi Xin7Huiling Chen8Xinsheng Ma9College of Applied Technology, Changchun University of Technology, Changchun, Jilin 130012, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaDepartment of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, ChinaSchool of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Computer Science, Birzeit University, Birzeit, State of PalestineDepartment of Information Technology, College of Computers and Information Technology, P.O. Box 11099, Taif University, Taif 21944, Saudi ArabiaSchool of Information Engineering, Hangzhou Medical College, Hangzhou 311300, Zhejiang, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaDepartment of Mathematics, Zhejiang International Studies University, Hangzhou 310023, ChinaThe whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA further balances the exploration and exploitation phases and makes it easier for the algorithm to get rid of the local optimum. To show the proposed method’s performance, MEWOA is contrasted to other superior algorithms on a series of comprehensive benchmark functions and applied to practical engineering problems. The experimental data reveal that the MEWOA is better than the contrast algorithms in convergence speed and solution quality. Hence, it can be concluded that MEWOA has great potential in global optimization.http://dx.doi.org/10.1155/2021/8130378 |
| spellingShingle | GuoChun Wang Wenyong Gui Guoxi Liang Xuehua Zhao Mingjing Wang Majdi Mafarja Hamza Turabieh Junyi Xin Huiling Chen Xinsheng Ma Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks Complexity |
| title | Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks |
| title_full | Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks |
| title_fullStr | Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks |
| title_full_unstemmed | Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks |
| title_short | Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks |
| title_sort | spiral motion enhanced elite whale optimizer for global tasks |
| url | http://dx.doi.org/10.1155/2021/8130378 |
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