An Extensive Investigation of Meta-Heuristics Algorithms for Optimization Problems

Abstract

Metaheuristic algorithms represent a class of optimization techniques tailored to tackle intricate problems that defy resolution through conventional means. Drawing inspiration from natural phenomena like genetics, swarm dynamics, and evolution, these algorithms traverse expansive search spaces in pursuit of identifying the optimal solution to a given problem. Well-known examples include genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search. These methodologies find widespread application across diverse domains such as engineering, finance, and computer science. Spanning several decades, the evolution of metaheuristic algorithms entails the refinement and diversification of optimization strategies rooted in natural systems. As indispensable tools in addressing complex optimization challenges across various fields, metaheuristic algorithms are poised to remain pivotal in driving technological advancements and fostering novel applications.