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Multi-objective particle swarm optimization based on cooperative hybrid strategy

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Abstract

A multi-objective particle swarm optimization based on cooperative hybrid strategy (CHSPSO) is presented in this paper to solve complex multi-objective problems. Most algorithms usually contain only one strategy, which makes them unable to trade off the convergence and diversity when solving the complex multi-objective problems. The proposed cooperative hybrid strategy can effectively guarantee the convergence and the diversity of the algorithm. The multi-population strategy and the dynamic clustering strategy are employed to improve the convergence and the diversity. At the same time, the life strategy and lottery probability selection strategy are used to further ensure the diversity of the population. A series of test functions are used to verify the effectiveness of CHSPSO. The performance of the proposed algorithm is compared with other evolutionary algorithms. The results show that CHSPSO can obtain a better convergence and diversity for the complex multi-objective problems.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant 61403249.

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  1. YuJia Wang contributed equally to this work

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Long Teng Road, Shanghai, 201620, China

    Hui Yu, YuJia Wang & ShanLi Xiao

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  1. Hui Yu
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  2. YuJia Wang
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  3. ShanLi Xiao
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Correspondence to YuJia Wang.

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Yu, H., Wang, Y. & Xiao, S. Multi-objective particle swarm optimization based on cooperative hybrid strategy. Appl Intell 50, 256–269 (2020). https://doi.org/10.1007/s10489-019-01496-3

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