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Empirical study of particle swarm optimization inspired by Lotka–Volterra model in Ecology

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Abstract

Particle swarm optimization (PSO) has been proved to be an effective technique in solving complex global optimization problems. Many modified versions of the original PSO algorithm emerged during the last 15 years. Many of those existing methods employ all particles in a single population which adopts the similar monotonic strategy. The loss of diversity resulted in the premature convergence problem. In this paper, we proposed a suite of multi-swarm Lotka–Volterra model inspired particle swarm optimization algorithms (MSLVPSO) to address the premature convergence problem. The intraspecific and interspecific cooperation and competition strategy of the proposed model dramatically increased diversity of particles. As a result, it makes the particles more likely to break away from the local optimum. In addition, we derived the method to set parameters and developed several cooperative–competitive schemes. We evaluated the proposed MSLVPSO algorithms using a variety of benchmark functions. We also compared our proposed method with typical single-swarm PSO algorithms. Our experimental results show that the proposed MSLVPSO optimizers outperform other state-of-the-art algorithms.

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Acknowledgements

This research was supported in part by National Natural Science Foundation of China under Grants 61671356, 61571346, 61601352, 61704127 and 61105066, in part by Scientific Research Program Funded by Shaanxi Provincial Education Department under Grant 17JK0989, in part by Fundamental Research Funds for the Central Universities under Grant JB141305. In addition, we are grateful to the anonymous reviewers and editors for their valuable suggestions and comments on the initial version of the manuscript.

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Authors and Affiliations

  1. Institute of Intelligent Control and Image Engineering, School of Aerospace Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Xianxiang Wu, Muye Sun, Xiao Chen & Baolong Guo

  2. School of Technology and Engineering, Xi’an Fanyi University, Xi’an, 710105, Shaanxi, People’s Republic of China

    Juan Wang

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  1. Xianxiang Wu
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  4. Juan Wang
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Correspondence to Xianxiang Wu.

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Wu, X., Sun, M., Chen, X. et al. Empirical study of particle swarm optimization inspired by Lotka–Volterra model in Ecology. Soft Comput 23, 5571–5582 (2019). https://doi.org/10.1007/s00500-018-3215-9

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