Metaheuristic Algorithms in Optimization and its Application: A Review

Metaheuristic algorithms are an intelligent way of thinking and working developed for resolving diverse issues about optimization. The number of potential solutions for such problems often is too large to be properly analyzed using standard procedures; thus, these algorithms are highly flexible and can be useful in many cases where needed to predict different types of optimizations accurately. Metaheuristics take inspiration from several natural processes like evolution or animal behavior, which allow them to show strength without being specific only towards one area. Some Metaheuristics algorithms are commonly being used like : Genetic Algorithm (GA), Simulated Annealing (SA), Evolutionary Algorithm (EA), Tabu Search (TS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Cuckoo Search Approach (CSA). All of them derives from this initial set of solutions and employ heuristics to get from this set of solutions.. The objective of this paper is to thoroughly analyze different metaheuristic algorithms. Their principles, mechanisms and the area where they are applied and will delve into. This paper provides a qualitative analysis of these algorithmic performances in diverse settings that underscore their strong suits as well as their weaknesses. The discourse also makes mention of some specific examples like how metaheuristic algorithms find utility application in various fields which include but are not limited to engineering or computer science, even economics and healthcare later down the line receive due consideration with an eye towards specific results; showing not only how effective these individual algorithms can be when applied under differing scenarios but also pointing out areas deserving further research efforts be directed onto them.