Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
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3690624.3709181.pdf
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2.51 MB
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Adobe PDF
Checksum (MD5)
7a47427033f0ce2d6bba4a5b72f2c020
Author(s)
Li, Qi
Cao, Zhiguang
Ma, Yining
Wu, Yaoxin
Gong, Yue-Jiao
Date Issued
July 20, 2025
Publisher
ACM|Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1
Citation
Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, and Yue-Jiao Gong. 2025. Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 (KDD '25). Association for Computing Machinery, New York, NY, USA, 683–694.
Version
Final published version
Abstract
Existing neural methods for the Travelling Salesman Problem (TSP) mostly aim at finding a single optimal solution. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structured policy. Concretely, on the one hand, a Relativization Filter (RF) is designed to enhance the robustness of the encoder to affine transformations of the instances, so as to potentially improve the quality of the found solutions. On the other hand, a Multi-Attentive Adaptive Active Search (MA3S) is tailored to allow the decoders to strike a balance between the optimality and diversity. Experimental evaluations on benchmark instances demonstrate the superiority of our method over recent neural baselines across different metrics, and its competitive performance against state-of-the-art traditional heuristics with significantly reduced computational time, ranging from 1.3× to 15× faster. Furthermore, we demonstrate that our method can also be applied to the Capacitated Vehicle Routing Problem (CVRP).
Description
KDD ’25, Toronto, ON, Canada
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3690624.3709181
