A Review of Classic Metaheuristic Optimization Algorithms

Metaheuristic algorithms are a category of optimization techniques derived from heuristic methods. These algorithms replicate natural phenomena, including evolutionary processes, annealing, and cooperative behavior of swarms, to solve a range of complex optimization problems. The present study initially elaborates the strengths and weaknesses of each algorithm in detail. It then moves on to the historical evolution of these algorithms, tracing their beginning to the advancements and applications made up to the present time. Finally, it examines the future courses of these algorithms through comparative simulation. This article aims to arm readers with well-rounded information on genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization techniques. Also, it shall enlighten them on the strength and weakness, past developments, future directions, and significance of milestone papers related to these computing methods.