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Particle swarm optimization with mutation operations controlled by landscape modality estimation using hill-valley detection

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Abstract

Particle swarm optimization (PSO) is one of swarm intelligence algorithms and has been used to solve various optimization problems. Since the performance of PSO is much affected by the algorithm parameters of PSO, studies on adaptive control of the parameters have been done. Adaptive PSO (APSO) is one of representative studies. Parameters are controlled according to the evolutionary state, where the state is estimated by distance relations among a best search point and other search points. Also, a global Gaussian mutation operation is introduced to escape from local optima. In this study, a new adaptive control based on landscape modality estimation using hill-valley detection is proposed. A proximity graph is created from search points, hills and valleys are detected in the graph, landscape modality of an objective function is identified as unimodal or multimodal. Parameters are adaptively controlled as: parameters for convergence are selected in unimodal landscape and parameters for divergence are selected in multimodal landscape. Also, two mutation operations are introduced according to the modality. In unimodal landscape, a new local mutation operation is applied to the worst hill point which will be moved toward the best point for convergence. In multimodal landscape, a new adaptive global mutation operation is applied to all hill points for escaping from local optima. The advantage of the proposed method is shown by comparing the results of the method with those by PSO with fixed parameters and APSO.

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Acknowledgments

This research is supported in part by Grant-in-Aid for Scientific Research (C) (No. 24500177, 26350443) of Japan society for the promotion of science.

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Authors and Affiliations

  1. Hiroshima City University, Hiroshima, Japan

    Tetsuyuki Takahama, Jun-ichi Kushida & Akira Hara

  2. Hiroshima Shudo University, Hiroshima, Japan

    Setsuko Sakai

Authors
  1. Tetsuyuki Takahama
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  2. Setsuko Sakai
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  3. Jun-ichi Kushida
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  4. Akira Hara
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Corresponding author

Correspondence to Tetsuyuki Takahama.

Additional information

This work was presented in part at the 1st International Symposium on Swarm Behavior and Bio-Inspired Robotics, Kyoto, Japan, October 28–30, 2015.

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Takahama, T., Sakai, S., Kushida, Ji. et al. Particle swarm optimization with mutation operations controlled by landscape modality estimation using hill-valley detection. Artif Life Robotics 21, 423–433 (2016). https://doi.org/10.1007/s10015-016-0299-0

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