Electromagnetic optimization based on an improved diversity‐guided differential evolution approach and adaptive mutation factor

Abstract

PurposeThe 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/approachAn adaptive mutation factor and attractive/repulsive phases guided by population diversity are used within the framework of DE algorithms.FindingsThe 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/implicationsAlthough the chosen benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.Practical implicationsThe proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.Originality/valueThis paper introduces the use of population diversity in order to guide the attractive/repulsive behavior of DE algorithms.