Solving the Travelling Salesman Problem for Efficient Route Planning through Swarm Intelligence-Based Optimization

With the rapid growth in global trade, the demand for efficient route planning and resource utilization in logistics and transportation mirrors the Travelling Salesman Problem (TSP). TSP refers to finding the shortest route possible of N destinations by visiting each destination once and returning to the starting point. Moreover, the computational complexity of TSP increases exponentially with the number of destinations, where finding an exact solution is not practical in larger instance. It has long been a challenging optimization problem, prompting the development of various methodologies to seek for more efficient solution, especially towards metaheuristics in recent research. Therefore, this research proposes an optimization algorithm with the implementation of the Swarm Intelligence-based method for solving TSP, providing an approximate solution. The proposed algorithm is evaluated by comparing its performance in terms of solution quality and computation time to well-known optimization methods, namely the Genetic Algorithm and the Ant Colony Optimization. 47 cities and 50 landmarks in the U.S. are selected as the destinations for two experimental datasets respectively with geospatial data retrieved from Google Maps Platform API. The experiment result suggests that the proposed algorithm has computed a near-optimal solution along with the shortest computation time among the three optimization methods. Solving the TSP efficiently contributes significantly to route planning for transportation and logistics. By shortening the travelling time, optimizing resource utilization, and minimizing fuel and energy consumption, this research further aligns with the global goal of carbon reduction for transportation and logistics systems.