Crossover Rate Sorting in Adaptive Differential Evolution

Abstract

Differential evolution (DE) is a popular and efficient heuristic numerical optimization algorithm that has found many applications in various fields. One of the main disadvantages of DE is its sensitivity to parameter values. In this study, we investigate the effect of the previously proposed crossover rate sorting mechanism on modern versions of DE. The sorting of the crossover rates, generated by a parameter adaptation mechanism prior to applying them in the crossover operation, enables the algorithm to make smaller changes to better individuals, and larger changes to worse ones, resulting in better exploration and exploitation. The experiments in this study were performed on several modern algorithms, namely L-SHADE-RSP, NL-SHADE-RSP, NL-SHADE-LBC and L-NTADE and two benchmark suites of test problems, CEC 2017 and CEC 2022. It is shown that crossover rate sorting does not result in significant additional computational efforts, but may improve results in certain scenarios, especially for high-dimensional problems.