Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments

Abstract

Several algorithms aimed at dynamic optimisation problems have been developed. This paper reports on the incorporation of a self-adaptive Brownian radius into competitive differential evolution (CDE). Four variations of a novel technique to achieving the self-adaptation is suggested and motivated. An experimental investigation over a large number of benchmark instances is used to determine the most effective of the four variations. The new algorithm is compared to its base algorithm on an extensive set of benchmark problems and its performance analysed. Finally, the new algorithm is compared to other algorithms by means of reported results found in the literature. The results indicate that CDE is improved the the incorporation of the self-adaptive Brownian radius and that the new algorithm compares well with other algorithms.