This study presents a comprehensive comparative analysis of nine state-of-the-art metaheuristic optimization algorithms applied to the classical Traveling Salesman Problem (TSP), a fundamental benchmark in combinatorial optimization. The selected algorithms—Ant Colony Optimization (ACO), Lion Algorithm (LA), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Vibrating Particles System (VPS), Social Spider Optimization (SSO), Cat Swarm Optimization (CSO), Bat Algorithm (BA), and Artificial Bee Colony (ABC)—are evaluated on three standardized TSPLIB benchmark instances: berlin52, eil76, and pr1002. The evaluation framework encompasses multiple performance metrics, including best-found cost, mean solution quality, standard deviation, and convergence behavior, over 30 independent runs per instance. The results offer empirical insights into each algorithm’s strengths, limitations, and scalability across problem sizes. Notably, ACO, GWO, and CSO demonstrate superior balance between solution accuracy and robustness, making them promising candidates for large-scale combinatorial problems. This work not only provides an up-to-date performance landscape of leading swarm-based and evolutionary metaheuristics but also guides algorithm selection for real-world optimization applications requiring adaptability and computational efficiency.
Almufti et al. (Wed,) studied this question.
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