Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem
Jiongzhi Zheng, Kun He, Jianrong Zhou, Jin Yan, Chu-Min Li
Abstract
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, which combines three reinforcement learning methods (Q-learning, Sarsa and Monte Carlo) with the well-known TSP algorithm, called Lin-Kernighan-Helsgaun (LKH). VSR-LKH replaces the inflexible traversal operation in LKH, and lets the program learn to make choice at each search step by reinforcement learning. Experimental results on 111 TSP benchmarks from the TSPLIB with up to 85,900 cities demonstrate the excellent performance of the proposed method.
Topics & Concepts
Travelling salesman problemReinforcement learningTree traversalComputer scienceMathematical optimizationVariable neighborhood searchMonte Carlo methodAlgorithmArtificial intelligenceMathematicsMetaheuristicStatisticsVehicle Routing Optimization MethodsMetaheuristic Optimization Algorithms ResearchReinforcement Learning in Robotics