Metaheuristic Algorithms and Their Applications in Different Fields

Summary

A potent method for resolving challenging optimization issues is provided by metaheuristic algorithms, which are heuristic optimization approaches. They provide an effective technique to explore huge solution spaces and identify close to ideal or optimal solutions. They are iterative and often inspired by natural or social processes. This study provides comprehensive information on metaheuristic algorithms and the many areas in which they are used. Heuristic optimization algorithms are well‐known for their success in handling challenging optimization issues. They are a potent tool for problem‐solving. Twenty well‐known metaheuristic algorithms, such as the tabu search, particle swarm optimization, ant colony optimization, genetic algorithms, simulated annealing, and harmony search, are included in the article. The article extensively explores the applications of these algorithms in diverse domains such as engineering, finance, logistics, and computer science. It underscores particular instances where metaheuristic algorithms have found utility, such as optimizing structural design, controlling dynamic systems, enhancing manufacturing processes, managing supply chains, and addressing problems in artificial intelligence, data mining, and software engineering. The paper provides a thorough insight into the versatile deployment of metaheuristic algorithms across different sectors, highlighting their capacity to tackle complex optimization problems across a wide range of real‐world scenarios.