Charting New Routes: Comparing Swarm-Based Approaches to the Traveling Salesman Problem

To solve the Traveling Salesman Problem (TSP), this research compares three swarmbased optimization algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Elephant Herding Optimization (EHO).Finding the shortest path to visit each city once and return to the starting point is the goal of the traditional combinatorial optimization problem, TSP.Exact techniques such as Branch and Bound (BB) and Dynamic Programming (DP) can effectively handle smaller TSP cases, but they become unfeasible as the number of cities increases.The solutions offered by metaheuristic algorithms are more scalable.The algorithms' performance is assessed in this study based on execution time, scalability, and solution quality for a range of city sizes (5 to 150).Results reveal that EHO surpasses the others in achieving lower optimal costs.