Gaussian Mutation Specular Reflection Learning with Local Escaping Operator Based Artificial Electric Field Algorithm and Its Engineering Application

During the contribution of a metaheuristic algorithm for solving complex problems, one of the major challenges is to obtain the one that provides a well-balanced exploration and exploitation. Among the possible solutions to overcome this issue is to combine the strengths of the different methods. In this study, one of the recently developed metaheuristic algorithms, artificial electric field algorithm (AEFA), has been used, to improve its converge speed and the ability to avoid the local optimum points of the given problems. To address these issues, Gaussian mutation specular reflection learning (GS) and local escaping operator (LEO) have been added to the essential steps on AEFA and called GSLEO-AEFA. In order to observe the effect of the applied features, 23 benchmark functions as well as engineering and real-world application problems were tested and compared with the other algorithms. Friedman and Wilcoxon rank-sum statistical tests, and complexity analyses were also conducted to measure the performance of GSLEO-AEFA. The overall effectiveness of the algorithm among the compared algorithms obtained in between 84.62–92.31%. According to the achieved results, it can be seen that GSLEO-AEFA has precise optimization accuracy even in changing dimensions, especially in engineering optimization problems.