Performance Comparison of Electromagnetism-Like Algorithms for Global Optimization

Abstract

Electromagnetism-like (EML) algorithm is a new evolutionary algorithm that bases on the electromagnetic attraction and repulsion among particles. It was originally proposed to solve optimization problems with bounded variables. Since its inception, many variants of the EML algorithm have been proposed in the literature. However, it remains unclear how to simulate the electromagnetic heuristics in an EML algorithm effectively to achieve the best performance. This study surveys and compares the EML algorithms in the literature. Furthermore, local search and perturbed point are two techniques commonly used in an EML algorithm to fine tune the solution and to help escaping from local optimums, respectively. Performance study is conducted to understand their impact on an EML algorithm.