A review of metaheuristic optimization methods

Abstract

In this paper, the metaheuristic optimization methods are categorized. Each method is explained detailly in basic principle and working mechanism. The article is separated to four parts. The second part shows the principle and implementation for each method such as GA, PSO, ACO and TS. The algorithms are categorized according to their searching type. The third part shows the combination of different optimization methods used to solve complex, multi-objective, high-dimensional problems. The fourth part is the future and challenges for metaheuristic optimization. The paper includes recent experiments to 2022.