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Learning TSP Combinatorial Search and Optimization with Heuristic Search

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1791))

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Abstract

Traveling Salesman Problem (TSP) and similar combinatorial search and optimization problems have many real-world applications in logistics, transportation, manufacturing, IC design, and other industries. Large-scale TSP tasks have always been challenging to solve fast. During the training phase of the model, when the number of city nodes exceeds 200, the training process will be terminated due to insufficient memory. This paper achieves reducing memory usage by simplifying the network model. However, the prediction accuracy is lowered after the network model is simplified. In this paper, heuristic search methods such as greedy search, beam search and 2-opt search are used to improve the prediction accuracy. Our main contributions are: increase the number of city nodes that can be solved from 100 to 1000; compensate for the loss of accuracy with various search techniques; use various search techniques in combinatorial search and optimization domain. The novelty of our paper is: the model structure of the Transformer is simplified, and various heuristic search techniques are used to compensate for the accuracy of the solution. In the inference stage, although the search time required by greedy search, beam search, and 2-opt search is quite different, all of them can improve the model’s prediction accuracy to varying degrees. Extensive experiments demonstrate that using various heuristic search techniques can greatly improve the prediction accuracy of the model.

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Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, 100084, China

    Hua Yang

  2. Tsinghua University, Beijing, 100084, China

    Ming Gu

Authors
  1. Hua Yang
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  2. Ming Gu
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Correspondence to Hua Yang .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Yang, H., Gu, M. (2023). Learning TSP Combinatorial Search and Optimization with Heuristic Search. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_34

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  • DOI: https://doi.org/10.1007/978-981-99-1639-9_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1638-2

  • Online ISBN: 978-981-99-1639-9

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