Metaheuristic Evolutionary Algorithms: Types, Applications, Future Directions, and Challenges
Kalpesh Shelke, Subhash K. Shinde · 2023
Metaheuristic optimization methods are widely used to solve complex optimization problems in various fields such as engineering, finance, and logistics. Evolutionary Algorithms (EAs) are a family of metaheuristic optimization algorithms that are inspired by biological evolution and natural selection. EAs mimic the process of natural selection by maintaining a population of candidate solutions and applying genetic operators such as mutation, crossover, and selection to generate new solutions over multiple generations. In this review, we will focus on six popular types of EAs: Genetic Algorithms (GA), Evolution Strategies (ES), Genetic Programming (GP), Differential Evolution (DE), Estimation of Distribution Algorithms (EDA), and Cultural Algorithms (CA). The study also provides insights into the selection of appropriate metaheuristic optimization methods for solving specific optimization problems.