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Differential evolution with variable leader-adjoint populations
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AI
DOI：10.1007/s10489-022-04290-w.png)
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Abstract
En 中文
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.
Keywords:
Differential evolution
Leader-adjoint model
Mutation strategies
Exploration and exploitation
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