Metaheuristics for Machine Learning

1Metaheuristic Algorithms and Their Applications in Different Fields: A Comprehensive Review

Abrar Yaqoob¹*, Navneet Kumar Verma² and Rabia Musheer Aziz¹

¹School of Advanced Science and Language, VIT Bhopal University, Kothrikalan, Sehore, India

²State Planning Institute (New Division), Planning Department Lucknow, Utter Pradesh, India

Abstract

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 ...