Electromagnetic optimization based on an improved diversity‐guided differential evolution approach and adaptive mutation factor
Leandro dos Santos Coelho, Piergiorgio Alotto · COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering · 2009
Purpose The purpose of this paper is to show, on a widely used benchmark problem, that adaptive mutation factors and attractive/repulsive phases guided by population diversity can improve the search ability of differential evolution (DE) algorithms. Design/methodology/approach An adaptive mutation factor and attractive/repulsive phases guided by population diversity are used within the framework of DE algorithms. Findings The paper shows that the combined use of adaptive mutation factors and population diversity in order to guide the attractive/repulsive behavior of DE algorithms can provide high‐quality solutions with small standard deviation on the selected benchmark problem. Research limitations/implications Although the chosen benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results. Practical implications The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems. Originality/value This paper introduces the use of population diversity in order to guide the attractive/repulsive behavior of DE algorithms.