Enhancing differential evolution algorithm with repulsive behavior

Abstract

In the real world, differential evolution (DE) algorithm can effectively solve optimization problems in engineering; thus, DE has been applied in various fields. However, in complex multimodal problems, DE may encounter stagnation during iterations. Thus, we propose an improved DE algorithm with repulsive behavior, named RBDE. The core idea of RBDE is that offsprings no longer simply learn from the current optima but continue to explore the direction in which the current optimal individual is repelled by poorer individuals. This mechanism increases the diversity of the learning direction of a population. RBDE includes two types of repulsive behaviors: In the first, RBDE selects two parents as the source of repulsion and generates two different repulsive forces to promote the offspring to explore the optimal individual; the other considers that the gradient of the repulsion between the parents is the learning direction of the offspring. The repulsive behavior can effectively alleviate the stagnation of DE when dealing with multimodal problems. To evaluate the performance of RBDE, we use CEC2017 benchmarks to test RBDE and nine other algorithms. The results show that the performance of RBDE is better than that of the other nine algorithms. In addition, RBDE is used to train an artificial neural network and is applied to the optimization problem of four-bar linkages, whose results indicate that the model obtained by RBDE is more accurate than those by the other algorithms.