Adaptive Parameter Control Using Search State Estimation and Extreme Individuals for Differential Evolution

Abstract

The performance of differential evolution (DE) is influenced by various factors such as algorithm parameters and mutation strategies. JADE (adaptive DE with optional external archive) is recognized as one of the most successful studies on parameter control in DE. In this study, in order to improve the search efficiency of JADE, we propose to integrate two methods we have proposed. One is the method to estimate whether the population of candidate solutions is converging or moving for particle swarm optimization. The normalized distance between the population center and the best solution is used for the estimation. When the population is converging, the search mode is adjusted to enhance the convergence. When the population is moving, the search mode is adjusted to enhance the movement. The other is the method to control DE parameters for extreme individuals in order to improve the search efficiency. The methods are modified to use the former method in JADE and to reduce the interference between the two methods. The effectiveness of JADE incorporating the proposed methods is shown by solving thirteen benchmark problems.