A Brief Review of Swarm Optimization Algorithms for Electrical Engineering and Computer Science Optimization Challenges

Abstract

Swarm intelligence (SI), a crucial element in the science of artificial intelligence, is steadily gaining importance as more and more high complexity problems demand solutions that may be imperfect but are nonetheless doable within a reasonable amount of time. Swarm intelligence, which takes most of its inspiration from biological systems, imitates how gregarious groups of animals work together to survive. This paper aims to discuss the key ideas, highlight potential application domains, their variations, and provide a thorough analysis of three SI algorithms, including the Dragonfly algorithm (DA), Grey wolf optimization (GWO), Whale optimization algorithm (WOA), and their applicability to many branches of electrical engineering and computer science. Finally, these methods were applied to the Zakharov, Levy, Sum Squares, and Drop Wave functions to calculate their algorithmic cost. According to the study, WOA outperforms the other two algorithms for the Levy and Zakharov functions, while GWO and DA perform better for the Sum Squares and Drop Wave functions, respectively.