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Opposition-based particle swarm optimization with adaptive mutation strategy

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Abstract

To solve the problem of premature convergence in traditional particle swarm optimization (PSO), an opposition-based particle swarm optimization with adaptive mutation strategy (AMOPSO) is proposed in this paper. In all the variants of PSO, the generalized opposition-based PSO (GOPSO), which introduces the generalized opposition-based learning (GOBL), is a prominent one. However, GOPSO may increase probability of being trapped into local optimum. Thus we introduce two complementary strategies to improve the performance of GOPSO: (1) a kind of adaptive mutation selection strategy (AMS) is used to strengthen its exploratory ability, and (2) an adaptive nonlinear inertia weight (ANIW) is introduced to enhance its exploitative ability. The rational principles are as follows: (1) AMS aims to perform local search around the global optimal particle in current population by adaptive disturbed mutation, so it can be beneficial to improve its exploratory ability and accelerate its convergence speed; (2) because it makes the PSO become rigid to keep fixed constant for the inertia weight, ANIW is used to adaptively tune the inertia weight to balance the contradiction between exploration and exploitation during its iteration process. Compared with several opposition-based PSOs on 14 benchmark functions, the experimental results show that the performance of the proposed AMOPSO algorithm is better or competitive to compared algorithms referred in this paper.

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Acknowledgments

This study was funded by the National Natural Science Foundation of China (No. 61170305, No. 61573157, No. 61562025), Natural Science Foundation of Guangdong Province of China (Grant No. 2014A030313454) and the Foundation of science, technology bureau of Liuzhou city of Guangxi Province of China (Grant No. 2014J020401).

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Authors and Affiliations

  1. Computer School, Wuhan University, Wuhan, 430072, Hubei, People’s Republic of China

    Wenyong Dong & Lanlan Kang

  2. School of Apply Science, Jiangxi University of Science and Technology, Ganzhou, 341000, People’s Republic of China

    Lanlan Kang

  3. State Key Laboratory of Intelligent Control and Management of Complex Systems at Institute of Automation Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Wensheng Zhang

Authors
  1. Wenyong Dong
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  2. Lanlan Kang
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  3. Wensheng Zhang
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Correspondence to Lanlan Kang.

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Wenyong Dong declares that he has no conflict of interest. Lanlan Kang declares that she has no conflict of interest. Wensheng Zhang declares that he has no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by V. Loia.

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Dong, W., Kang, L. & Zhang, W. Opposition-based particle swarm optimization with adaptive mutation strategy. Soft Comput 21, 5081–5090 (2017). https://doi.org/10.1007/s00500-016-2102-5

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