Comparative Analysis Of Nature-Inspired MetaHeuristic Optimization Algorithms

Abstract

Metaheuristic methods were utilized to determine the most appropriate solution to complicated problems in engineering applications, telecommunications issues, security issues, etc. This research and development has become a prime concern in the ever-evolving age of technologies. Nowadays, many metaheuristic algorithms are gaining popularity, like “Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).” This research provides an overview of the characteristics of a few metaheuristic optimization techniques. It contrasts “the Firefly Algorithm (FA), Artificial Bee Colony (ABC) Algorithm, Cuckoo Search (CS), and Whale Optimization Algorithm with the Particle Swarm Optimization (PSO) ”.The purpose of this study is to assess and evaluate the publications from 2010 to 2023 using a number of variables, which include (a) The value of the reviewed studies based on the year of publication.(b) Studies comparing PSO to metaheuristic algorithms (c) Performance evaluation of comparative algorithms (d) Inspirational approaches and early proposed studies and years for metaheuristic algorithms and (e) Metaheuristic algorithms compared to PSO research. In-depth comparisons between PSO and the most widely used metaheuristic algorithms are made in this work.