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

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 desig...