Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA
This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The M...
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MDPI AG
2024-10-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/10/639 |
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| author | Chunfang Li Yuqi Yao Mingyi Jiang Xinming Zhang Linsen Song Yiwen Zhang Baoyan Zhao Jingru Liu Zhenglei Yu Xinyang Du Shouxin Ruan |
| author_facet | Chunfang Li Yuqi Yao Mingyi Jiang Xinming Zhang Linsen Song Yiwen Zhang Baoyan Zhao Jingru Liu Zhenglei Yu Xinyang Du Shouxin Ruan |
| author_sort | Chunfang Li |
| collection | DOAJ |
| description | This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm’s global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm’s efficiency and robustness. Ultimately, an extensive validation of MISWOA through “simulation + experimentation” approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms. |
| format | Article |
| id | doaj-art-d2dedc9b33bd433291422f266fd362e1 |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-d2dedc9b33bd433291422f266fd362e12025-08-20T02:11:14ZengMDPI AGBiomimetics2313-76732024-10-0191063910.3390/biomimetics9100639Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOAChunfang Li0Yuqi Yao1Mingyi Jiang2Xinming Zhang3Linsen Song4Yiwen Zhang5Baoyan Zhao6Jingru Liu7Zhenglei Yu8Xinyang Du9Shouxin Ruan10School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaThe People’s Liberation Army (PLA) Unit 63850 of China, Changchun 130022, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaFAW Tooling Die Manufacturing Co., Ltd., Changchun 130013, ChinaFAW Tooling Die Manufacturing Co., Ltd., Changchun 130013, ChinaCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130022, ChinaFAW Tooling Die Manufacturing Co., Ltd., Changchun 130013, ChinaChina FAW Group Corporation, Changchun 130000, ChinaThis paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm’s global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm’s efficiency and robustness. Ultimately, an extensive validation of MISWOA through “simulation + experimentation” approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.https://www.mdpi.com/2313-7673/9/10/639Whale Optimization AlgorithmMulti-Swarm Optimizationadaptive spiral indentation strategyglobal search capabilityoptimization algorithm robustness |
| spellingShingle | Chunfang Li Yuqi Yao Mingyi Jiang Xinming Zhang Linsen Song Yiwen Zhang Baoyan Zhao Jingru Liu Zhenglei Yu Xinyang Du Shouxin Ruan Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA Biomimetics Whale Optimization Algorithm Multi-Swarm Optimization adaptive spiral indentation strategy global search capability optimization algorithm robustness |
| title | Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA |
| title_full | Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA |
| title_fullStr | Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA |
| title_full_unstemmed | Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA |
| title_short | Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA |
| title_sort | evolving the whale optimization algorithm the development and analysis of miswoa |
| topic | Whale Optimization Algorithm Multi-Swarm Optimization adaptive spiral indentation strategy global search capability optimization algorithm robustness |
| url | https://www.mdpi.com/2313-7673/9/10/639 |
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