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Self-adaptive differential evolution algorithm with improved mutation mode

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Abstract

The optimization performance of the Differential Evolution algorithm (DE) is easily affected by its control parameters and mutation modes, and their settings depend on the specific optimization problems. Therefore, a Self-adaptive Differential Evolution algorithm with Improved Mutation Mode (IMMSADE) is proposed by improving the mutation mode of DE and introducing a new control parameters adaptation strategy. In IMMSADE, each individual in the population has its own control parameters, and they would be dynamically adjusted according to the population diversity and individual difference. IMMSADE is compared with the basic DE and the other state-of-the-art DE algorithms by using a set of 22 benchmark functions. The experimental results show that the overall performance of the proposed IMMSADE is better than the basic DE and the other compared DE algorithms.

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Acknowledgments

The authors would like to thank the reviewers for their critical and constructive review of the manuscript.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Sichuan University, Chengdu, 610065, China

    Shihao Wang & Hongyu Yang

  2. National Key Laboratory of Air Traffic Control Automation System Technology, Sichuan University, Chengdu, 610065, China

    Shihao Wang & Hongyu Yang

  3. Shanghai Electrical Apparatus Research Institute, Shanghai, 200063, China

    Yuzhen Li

Authors
  1. Shihao Wang
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  2. Yuzhen Li
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  3. Hongyu Yang
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Correspondence to Shihao Wang.

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

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Funding

This study was funded by National Natural Science Foundation of China (71573184).

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Cite this article

Wang, S., Li, Y. & Yang, H. Self-adaptive differential evolution algorithm with improved mutation mode. Appl Intell 47, 644–658 (2017). https://doi.org/10.1007/s10489-017-0914-3

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  • DOI: https://doi.org/10.1007/s10489-017-0914-3

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