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Self-adaptive differential evolution algorithm with improved mutation strategy

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Abstract

Different mutation strategies and control parameters settings directly affect the performance of differential evolution (DE) algorithm. In this paper, a self-adaptive differential evolution algorithm with improved mutation strategy (IMSaDE) is proposed to improve optimization performance of DE. IMSaDE improves the “DE/rand/2” mutation strategy by incorporating elite archive strategy and control parameters adaptation strategy. Both strategies diversify the population and improve the convergence performance of the algorithm. IMSaDE was compared with eleven DE algorithms and six non-DE algorithms by using a set of 20 benchmark functions taken from the literature. Experimental results show that the overall performance of IMSaDE is better than the other competitors. In addition, the size of elite population has a significant impact on the performance of IMSaDE.

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Acknowledgements

The authors would like to thank the reviewers for their critical and constructive review of the manuscript. This study was funded by National Natural Science Foundation of China (71573184).

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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 & Hong Liu

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

    Yuzhen Li

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  1. Shihao Wang
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  2. Yuzhen Li
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  3. Hongyu Yang
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  4. Hong Liu
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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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Communicated by V. Loia.

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Wang, S., Li, Y., Yang, H. et al. Self-adaptive differential evolution algorithm with improved mutation strategy. Soft Comput 22, 3433–3447 (2018). https://doi.org/10.1007/s00500-017-2588-5

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  • DOI: https://doi.org/10.1007/s00500-017-2588-5

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