A History-Driven Differential Evolution Algorithm for Optimization in Dynamic Environments

Abstract

This paper presents a novel differential evolution algorithm to solve dynamic optimization problems. In the proposed algorithm, the entire population is composed of several subpopulations, which are evolved independently and excluded each other by a predefined Euclidian-distance. In each subpopulation, the “DE/best/1” mutation operator is employed to generate a mutant individual in this paper. In order to fully exploit the newly generated individual, the selection operator was extended, in which the newly generated trial vector competed with the worst individual if this trial vector was worse than the target vector in terms of the fitness. Meanwhile, this trial vector was stored as the historical information, if it was better than the worst individual. When the environmental change was detected, some of the stored solutions were retrieved and expected to guide the reinitialized solutions to track the new location of the global optimum as soon as possible. The proposed algorithm was compared with several state-of-the-art dynamic evolutionary algorithms over the representative benchmark instances. The experimental results show that the proposed algorithm outperforms the competitors.