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

Abstract

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.

Citation

dos Santos Coelho, L. and Alotto, P. (2009), "Electromagnetic optimization based on an improved diversity‐guided differential evolution approach and adaptive mutation factor", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 28 No. 5, pp. 1112-1120. https://doi.org/10.1108/03321640910969377