Title: Variable-grouping-based exponential crossover for differential evolution algorithm

Authors: Shu Yang; Qiuling Huang; Laizhong Cui; Kunkun Xu; Zhong Ming; Zhenkun Wen

Addresses: College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China; Department of Computing, The Hong Kong Polytechnic University, Hung Hom, KL, Hong Kong ' College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China ' College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China ' College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China ' College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China ' College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

Abstract: The performance of differential evolution (DE) algorithm largely depends on its crossover operator, whose substantive characteristics are to make the algorithm search in a subspace of the original search space. Different crossover operators use different subspace divisions, and how to choose a suitable crossover operator for a specific optimisation problem is still an open issue. This paper proposes variable-grouping-based exponential crossover (VGExp), where all variables are divided into multiple groups based on interaction information, and the variables that are mutated simultaneously have a high probability of coming from the same group. Moreover, the solutions can improve the accuracy of the variable grouping and provide initial guidance for optimisation. Therefore, the proposed VGExp seamlessly combines variables grouping technique and differential evolution. The experiment results based on 30 CEC2014 test problems show that VGExp can improve the performance of most DE variants, and it is also better than other well-developed crossover operators.

Keywords: differential evolution; exponential crossover; variable grouping; variable interaction.

DOI: 10.1504/IJBIC.2020.107486

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.3, pp.147 - 158

Received: 30 Sep 2019
Accepted: 25 Nov 2019
Published online: 29 May 2020 *

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