Multi-strategies enhanced aquila optimizer for global optimization: Comprehensive review and comparative analysis
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| 發表在: | Journal of Computational Design and Engineering vol. 12, no. 5 (May 2025), p. 134-161 |
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| 主要作者: | |
| 其他作者: | , , |
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Oxford University Press
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| 在線閱讀: | Citation/Abstract Full Text - PDF |
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| Resumen: | This paper proposes 16 enhanced aquila optimizers with multiple strategies and applies them to the CEC2022 benchmark functions and six classic engineering application problems. The experimental comparative analysis results show that the performance of the random walk aquila optimizer (RWAO) and the crisscross aquila optimizer (CCAO) is significantly better than that of other enhanced aquila optimizers. Moreover, by comparing RWAO with over 10 existing powerful optimization techniques, it was found that RWAO has significant competitiveness. The Wilcoxon rank sum test results also proved that the RWAO and CCAO algorithms have significant differences from the basic aquila optimizer (AO), and the RWAO algorithm outperformed all the other enhanced aquila optimizers in optimizing engineering design problems. The experimental results show that the random walk and the crossover strategies can significantly enhance the optimization performance of the basic AO. The method presented in this paper has high reference value for improving the performance of other metaheuristic optimization algorithms. The detailed code publish website is https://ww2.mathworks.cn/matlabcentral/fileexchange/180254-the-sixteen-strategies-to-enhanced-ao-algorithms. |
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| ISSN: | 2288-5048 |
| DOI: | 10.1093/jcde/qwaf047 |
| Fuente: | Engineering Database |