DPSO‐Q: A Reinforcement Learning–Enhanced Swarm Algorithm for Solving the Traveling Salesman Problem

The rapid growth of e‐commerce has amplified the need for efficient logistics and delivery route planning. The Traveling Salesman Problem (TSP) provides a mathematical framework to address this challenge by finding optimal delivery routes. In this study, we propose a novel algorithm, DPSO‐Q, which synergizes the adaptability of reinforcement learning from Ant‐Q with the computational efficiency of Discrete Particle Swarm Optimization (DPSO). By leveraging swarm intelligence and adaptive learning mechanisms, DPSO‐Q achieves a balance between computational efficiency and high‐quality solutions. Experimental evaluations demonstrate its potential for large‐scale logistics optimization, making it a promising tool for addressing the complexities of modern supply chain systems. DPSO‐Q reduces tour lengths by up to 7.5% compared to DPSO and achieves execution times over 90% faster than ACO and Ant‐Q on standard datasets such as ch130 and zi929.