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 design problems. Originality/value – This paper introduces the use of population diversity in order to guide the attractive/repulsive behavior of DE algorithms.