Metaheuristic optimization algorithms: An overview

Abstract

Metaheuristic optimization algorithms are versatile and adaptable tools that effectively solve various complex optimization problems. These algorithms are not restricted to specific types of problems or gradients. They can explore globally and handle multi-objective optimization efficiently. They strike a balance between exploration and exploitation, contributing to advancements in optimization. However, it’s important to note their limitations, including the lack of a guaranteed global optimum, varying convergence rates, and their somewhat opaque functioning. In contrast, metaphor-based optimization algorithms, while intuitively appealing, have faced controversy due to potential oversimplification and unrealistic expectations. Despite these considerations, metaheuristic algorithms continue to be widely used for tackling complex problems. This research paper aims to explore the fundamental components and concepts that underlie optimization algorithms, focusing on the use of search references and the delicate balance between exploration and exploitation. Visual representations of the search behavior of selected metaheuristic algorithms will also be provided.