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Xujie Tan
Jiujiang University

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Differential evolution algorithm of soft island model based on K-means clustering Xujie Tan; Seong-Yoon Shin
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1548-1555

Abstract

Differential evolution (DE) is a highly effective evolutionary algorithm. However, the performance of DE depends on strategies and control parameters. The combination of many strategies helps balance the exploitation and exploration of DE. In this study, a multi-population based on k-means clustering is proposed to realize an ensemble of multiple strategies, thereby resulting in a new DE variant, namely KSDE, where similar individuals in the population implement the same mutation strategies, and dissimilar subpopulations migrate information through the soft island model (SIM). Firstly, the population is virtually divided into k subpopulations by the k-means clustering algorithm. Secondly, the individual specific mutation scheme is selected from a strategy pool by random method. Finally, the migration of subpopulation information is done using soft island model. The performance of the KSDE algorithm is evaluated on 13 benchmark problems. The experiments show that KSDE algorithm improves the performance of the DE algorithm.
Co-Authors Seong-Yoon Shin