Population interaction network in representative differential evolution algorithms: Power-law outperforms Poisson distribution

Xiaosi Li, Jiayi Li, Haichuan Yang, Yirui Wang and Shangce Gao

Physica A: Statistical Mechanics and its Applications, 2022, vol. 603, issue C

Abstract: Differential evolution is a classical and effective evolutionary algorithm. In recent years, many differential evolution variants have been proposed and achieved good results on many problems. To investigate their inherent characteristics, this paper uses the population interaction network. Six representative differential evolution algorithms including DE, JADE, CJADE, SHADE, L-SHADE, and EBLSHADE are analyzed from the perspective of information interaction among individuals. The cumulative distribution function of degrees of nodes obtained from the population interaction network on thirty IEEE CEC2017 benchmark functions is fitted by seven distribution models. Results show that the cumulative distribution function of differential evolution is the Poisson distribution whereas the other variants meet the Power-law distribution. The Power-law distribution influences their performance and depends on the population size. These remarkable findings suggest that the Power-law distribution widely exists in best-performing differential evolution algorithms, which gives empirical evidence for designing Power-law distribution-based differential evolution algorithms.

Keywords: Differential evolution; Evolutionary algorithms; Complex network; Population interaction network; Cumulative distribution function (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122005052

DOI: 10.1016/j.physa.2022.127764

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