Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

Authors

  • Zhang-Hua Fu Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong, Shenzhen
  • Kai-Bin Qiu The Chinese University of Hong Kong, Shenzhen
  • Hongyuan Zha Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong, Shenzhen

Keywords:

Optimization, Planning/Scheduling and Learning, Routing, Constraint Optimization

Abstract

For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of arbitrarily large size, based on a series of techniques such as graph sampling, graph converting and heat maps merging. Furthermore, the heat maps are fed into a reinforcement learning approach (Monte Carlo tree search), to guide the search of high-quality solutions. Experimental results based on a large number of instances (with up to 10,000 vertices) show that, this new approach clearly outperforms the existing machine learning based TSP algorithms, and significantly improves the generalization ability of the trained model.

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Published

2021-05-18

How to Cite

Fu, Z.-H., Qiu, K.-B., & Zha, H. (2021). Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7474-7482. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16916

Issue

Section

AAAI Technical Track on Machine Learning I