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Differential Evolution Algorithm with Three Mutation Operators for Global Optimization

Author

Listed:
  • Xuming Wang

    (Engineering Training Center, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Xiaobing Yu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly chosen from the three categories, and three parameter values are also randomly selected from the parameter pool. The three groups of mutation operators and parameter values are employed to produce trial vectors. The proposed algorithm makes good use of different mutation operators. Three recently proposed differential evolution variants and three non-differential evolution algorithms are used to make comparisons on the 29 testing functions from CEC. The experimental results have demonstrated that the proposed algorithm is very competitive. The proposed algorithm is utilized to solve three real applications, and the results are superior.

Suggested Citation

  • Xuming Wang & Xiaobing Yu, 2024. "Differential Evolution Algorithm with Three Mutation Operators for Global Optimization," Mathematics, MDPI, vol. 12(15), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2311-:d:1441498
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    References listed on IDEAS

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    1. Tsafarakis, Stelios & Zervoudakis, Konstantinos & Andronikidis, Andreas & Altsitsiadis, Efthymios, 2020. "Fuzzy self-tuning differential evolution for optimal product line design," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1161-1169.
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