Meta Heuristic Algorithm
Arshiya Begum Mohammed, Asfia Sabahath · 2025
Metaheuristic algorithms represent a key area of optimization research that generates interest for many academicians and researchers in today's technology landscape. Researchers have been using these methods as a problem-solving approach for many decades when applied to combinatorial optimization problems, which has now grown into a significant research field through its incorporation of nature-inspired selection and evolutionary problem-solving principles. This study examines the basic principles of metaheuristic algorithms and discusses their key characteristics while categorizing them and comparing their common approaches. This paper examines Genetic Algorithms, Particle Swarm Optimization, Grey Wolf Optimization, Water Wave Optimization, and Ant Colony Optimization by reviewing their operational mechanisms and evaluating their strengths and drawbacks. This paper aims to enhance understanding and encourage the development of novel strategies that leverage these powerful optimization tools for emerging challenges.