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

Abstract

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.