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Improved differential evolution with dynamic mutation parameters

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Abstract

Differential evolution (DE) algorithms tend to be limited to local optimization when solving complex optimization problems. Different iteration schemes lead to different convergence speeds. In this paper, we mainly use the dynamic mutation parameter \(\text {FS}\) to improve the DE algorithm. Based on two ideas, a total of seven DE schemes are proposed to optimize the DE algorithm. We test the performance of the improved DE scheme on 56 test functions. Experiments show that the improved DE algorithm is better than the baseline DE algorithm in terms of accuracy, convergence and8 convergence speed.

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Funding

This work is supported in part by the National Natural Science Foundation of China under Grant 62206109 and the Innovation and Entrepreneurship Training Program for Undergraduate under Grant CX22356.

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Authors and Affiliations

  1. College of Cyber Security, Jinan University, Guangzhou, 511436, China

    Yifeng Lin, Yuer Yang & Yinyan Zhang

  2. Pazhou Lab, Guangzhou, China

    Yinyan Zhang

Authors
  1. Yifeng Lin
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  2. Yuer Yang
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  3. Yinyan Zhang
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Correspondence to Yinyan Zhang.

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Lin, Y., Yang, Y. & Zhang, Y. Improved differential evolution with dynamic mutation parameters. Soft Comput 27, 17923–17941 (2023). https://doi.org/10.1007/s00500-023-09080-1

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