Variable-grouping-based exponential crossover for differential evolution algorithm

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.