A Comparative Study of Metaheuristic Optimization Algorithms on Distinct Benchmark Functions

Abstract

Metaheuristic optimization algorithms are widely applied to tackle optimization problems across various fields. Recently, these algorithms have gained prominence over traditional deterministic methods for solving complex optimization issues. However, no single technique is universally effective for all types of optimization challenges. As a result, researchers have focused on enhancing existing metaheuristic methods or creating new ones. Numerous nature-inspired meta-heuristic algorithms have emerged to address complex optimization problems. Evaluating and comparing the performance of these algorithms using statistical analysis is essential for identifying the most effective ones. A mix of unimodal and multimodal functions have been selected to be tested against chosen algorithms for this study purpose. This study examines the performance of four metaheuristic optimization algorithms of Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Salp Swarm Algorithm (SSA). The results indicate that the GWO surpasses the other algorithms across the majority of evaluation metrics, including mean value, standard deviation, convergence rate, and computation time. DE achieves the second-best performance, with PSO ranking third, while SA exhibits the lowest overall performance.