Learning Improvement Heuristics for Solving Routing Problems
| Autor: | Yaoxin Wu, Wen Song, Andrew Lim, Zhiguang Cao, Jie Zhang |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Mathematical optimization Computer Science - Artificial Intelligence Computer Networks and Communications Computer science business.industry Deep learning Travelling salesman problem Machine Learning (cs.LG) Computer Science Applications Artificial Intelligence (cs.AI) Artificial Intelligence Vehicle routing problem Reinforcement learning Limit (mathematics) Artificial intelligence Routing (electronic design automation) Heuristics business Software Selection (genetic algorithm) |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 33:5057-5069 |
| ISSN: | 2162-2388 2162-237X |
| Popis: | Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by hand-crafted rules which may limit their performance. In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention based deep architecture as the policy network to guide the selection of next solution. We apply our method to two important routing problems, i.e. travelling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art deep learning based approaches. The learned policies are more effective than the traditional hand-crafted ones, and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions and even real-world dataset. Comment: 10 pages, 4 figures |
| Databáze: | OpenAIRE |
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