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Differential evolution with variable leader-adjoint populations

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Abstract

The performance of differential evolution (DE) is significantly affected by the selection of mutation strategies and control parameters. Inappropriate selection may lead to premature convergence and stagnation. Therefore, selecting appropriate mutation strategies and control parameters has always been a challenging task. In this paper, a differential evolution with variable leader-adjoint populations (LADE) is proposed. In LADE, a leader-adjoint model is used to divide the population in each generation into leader population and adjoint population. The leader population adopts a novel DE/current-best-rand/1 mutation strategy that can enhance the exploitation ability and avoid evolution stagnation. The adjoint population employs an improved DE/rand/1 mutation strategy that can not only strengthen the exploration ability, but also accelerate individual evolution by guiding the search process to the promising regions. Consequently, the leader-adjoint model can achieve a good balance between exploration and exploitation at different stages of evolution. Moreover, a parameter adaptation method is utilized to dynamically adjust the values of control parameters. To verify the performance of LADE, numerical experiments on the CEC2014 benchmark functions and Lennard-Jones potential real-world problem are executed. Experiment results show that the proposed LADE is significantly better than, or at least comparable to recent and advanced algorithms. In addition, experiments evaluate and analyze the effect of control parameters on the algorithm.

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Data availability

The CEC2014 benchmark functions used in this paper are available in [48]. The source code of the compared algorithms can be replicated according to the corresponding literature, and the proposed algorithm is available on reasonable request.

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Acknowledgments

The authors would like to thank the editors and reviewers for their helpful suggestions and comments on this paper.

Funding

This paper is supported by National Natural Science Foundation of China (Grant No: U20A20161, 62101363), Key Technology Research and Development Program of Henan Province (Grant No: 212102210532, 222102210041), and Project of Henan Police College (Grant No: HNJY202220).

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    Authors and Affiliations

    1. National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, 610065, China

      Yuzhen Li, Hongyu Yang & Hu Chen

    2. Department of Network Security, Henan Police College, Zhengzhou, 450046, China

      Shihao Wang

    3. College of Computer Science, Sichuan University, Chengdu, 610065, China

      Hongyu Yang & Hu Chen

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    1. Yuzhen Li
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    4. Hu Chen
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    Correspondence to Shihao Wang or Hongyu Yang.

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    Li, Y., Wang, S., Yang, H. et al. Differential evolution with variable leader-adjoint populations. Appl Intell 53, 15580–15602 (2023). https://doi.org/10.1007/s10489-022-04290-w

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