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An enhanced Mayfly optimization algorithm based on orthogonal learning and chaotic exploitation strategy

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Abstract

As a new method proposed to solve optimization problems, the mayfly algorithm that possesses the advantages of other advanced algorithms can play a very sound effect. However, there are still some shortcomings of local optimization and slow convergence speed when dealing with complex optimization problems. In this paper, two effective strategies are first integrated into the basic mayfly algorithm to enhance algorithm performance. Firstly, the orthogonal learning is applied to increase the diversity of primary male mayfly operators to guide the male mayfly to move more steadily, rather than oscillatory. Secondly, the chaotic exploitation is added to form the new position of an offspring to improve search capability. In order to verify the effectiveness of the enhanced algorithm, it is evaluated and compared with other excellent algorithms using benchmark functions. The Wilcoxon test, exploration–exploitation analysis and the time complexity analysis are also performed to analyze whether it yield promising results. In addition, three kinds of engineering optimization problems are also tested in the experiments including with constraints and without constraints. Computational results show that enhanced mayfly optimization algorithm achieves sound performance on all test problems and can attain high-quality solutions for different engineering optimization problems.

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Acknowledgements

This work was supported by Jiangsu Postdoctoral Research Foundation (Grant No. 2020Z410), Jiangsu Industry and University Cooperation Project (Grant No. BY2019006) and General Project of Natural Science Research in Universities of Jiangsu Province (Grant No. 19KJB460005).

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Authors and Affiliations

  1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, Jiangsu Province, 211816, China

    Dashuang Zhou, Zhengyang Kang, Xiaoping Su & Chuang Yang

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  1. Dashuang Zhou
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  2. Zhengyang Kang
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Correspondence to Xiaoping Su.

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Zhou, D., Kang, Z., Su, X. et al. An enhanced Mayfly optimization algorithm based on orthogonal learning and chaotic exploitation strategy. Int. J. Mach. Learn. & Cyber. 13, 3625–3643 (2022). https://doi.org/10.1007/s13042-022-01617-4

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