Compare the Performance of Meta-Heuristics Algorithm

Abstract

Metaheuristic algorithms have emerged as powerful optimization techniques capable of efficiently exploring complex solution spaces to find near-optimal solutions. This paper provides a comprehensive review and comparative analysis of several widely used metaheuristic algorithms, including genetic algorithms (GA), particle swarm optimization (PSO), firefly algorithm (FA), grey wolf optimizer (GWO), squirrel search algorithm (SSA), flying fox optimization algorithm (FFO). The comparative analysis encompasses various performance metrics, such as convergence speed, solution quality, robustness, scalability, and applicability across diverse problem domains. The study investigates the strengths and weaknesses of each algorithm through empirical evaluations of benchmark problems, highlighting their suitability for different optimization scenarios. Additionally, the impact of parameter tuning on algorithm performance is discussed, emphasizing the need for careful parameter selection to achieve optimal results.