Stochastic Opposition-Based Learning Using a Beta Distribution in Differential Evolution

Since it first appeared, differential evolution (DE), one of the most successful evolutionary algorithms, has been studied by many researchers. Theoretical and empirical studies of the parameters and strategies have been conducted, and numerous variants have been proposed. Opposition-based DE (ODE), one of such variants, combines DE with opposition-based learning (OBL) to obtain a high-quality solution with low-computational effort. In this paper, we propose a novel OBL using a beta distribution with partial dimensional change and selection switching and combine it with DE to enhance the convergence speed and searchability. Our proposed algorithm is tested on various test functions and compared with standard DE and other ODE variants. The results indicate that the proposed algorithm outperforms the comparison group, especially in terms of solution accuracy.