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A survey on deep learning-based algorithms for the traveling salesman problem

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  • Open access
  • Published: 12 December 2024
  • Volume 19, article number 196322, (2025)
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Frontiers of Computer Science Aims and scope Submit manuscript
A survey on deep learning-based algorithms for the traveling salesman problem
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  • Jingyan Sui1,2,
  • Shizhe Ding1,2,
  • Xulin Huang1,4,
  • Yue Yu1,2,5,
  • Ruizhi Liu1,2,
  • Boyang Xia1,2,
  • Zhenxin Ding1,2,
  • Liming Xu1,2,
  • Haicang Zhang1,2,
  • Chungong Yu1,2 &
  • …
  • Dongbo Bu1,2,3 

  • 1158 Accesses

  • Explore all metrics

Abstract

This paper presents an overview of deep learning (DL)-based algorithms designed for solving the traveling salesman problem (TSP), categorizing them into four categories: end-to-end construction algorithms, end-to-end improvement algorithms, direct hybrid algorithms, and large language model (LLM)-based hybrid algorithms. We introduce the principles and methodologies of these algorithms, outlining their strengths and limitations through experimental comparisons. End-to-end construction algorithms employ neural networks to generate solutions from scratch, demonstrating rapid solving speed but often yielding subpar solutions. Conversely, end-to-end improvement algorithms iteratively refine initial solutions, achieving higher-quality outcomes but necessitating longer computation times. Direct hybrid algorithms directly integrate deep learning with heuristic algorithms, showcasing robust solving performance and generalization capability. LLM-based hybrid algorithms leverage LLMs to autonomously generate and refine heuristics, showing promising performance despite being in early developmental stages. In the future, further integration of deep learning techniques, particularly LLMs, with heuristic algorithms and advancements in interpretability and generalization will be pivotal trends in TSP algorithm design. These endeavors aim to tackle larger and more complex real-world instances while enhancing algorithm reliability and practicality. This paper offers insights into the evolving landscape of DL-based TSP solving algorithms and provides a perspective for future research directions.

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Acknowledgements

This study was funded by the National Key R&D Program of China (2020YFA0907000), the National Natural Science Foundation of China (Grant Nos. 32270657, 32271297, 82130055, 62072435) and the Youth Innovation Promotion Association, Chinese Academy of Sciences. We appreciate the ComputeX center, ICT, CAS for providing computation service.

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Authors and Affiliations

  1. State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Jingyan Sui, Shizhe Ding, Xulin Huang, Yue Yu, Ruizhi Liu, Boyang Xia, Zhenxin Ding, Liming Xu, Haicang Zhang, Chungong Yu & Dongbo Bu

  2. University of Chinese Academy of Sciences, Beijing, 100190, China

    Jingyan Sui, Shizhe Ding, Yue Yu, Ruizhi Liu, Boyang Xia, Zhenxin Ding, Liming Xu, Haicang Zhang, Chungong Yu & Dongbo Bu

  3. Central China Institute of Artificial Intelligence, Zhengzhou, 450046, China

    Dongbo Bu

  4. Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450002, China

    Xulin Huang

  5. Hangzhou Institute for Advanced Study, UCAS, Hangzhou, 310024, China

    Yue Yu

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  1. Jingyan Sui
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Correspondence to Dongbo Bu.

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Additional information

Jingyan Sui is a PhD candidate at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. Her primary research interests encompass algorithm design, machine learning, deep learning, and combinatorial optimization.

Shizhe Ding is a PhD candidate at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. His primary research interests encompass machine learning, and combinatorial optimization.

Xulin Huang is a graduate student at Henan Institute of Advanced Technology, Zhengzhou University, and State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, China. His primary research interests encompass algorithm design, deep learning, combinatorial optimization, and bioinformatics.

Yue Yu is a graduate student at Hangzhou Institute for Advanced Study, and State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. Her main research interests encompass algorithm design, deep learning, and bioinformatics.

Ruizhi Liu is a PhD candidate at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. His main research interests encompass algorithm design, deep learning, combinatorial optimization, and chip design.

Boyang Xia is a graduate student at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. His main research interests encompass machine learning and combinatorial optimization.

Zhenxin Ding is a graduate student at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. His main research interests encompass algorithm design, machine learning, and combinatorial optimization.

Liming Xu is a graduate student at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. Her main research interests encompass algorithm design, machine learning, and combinatorial optimization.

Haicang Zhang is a PhD, an associate researcher, a Master’s supervisor at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. His main research focuses on machine learning, protein design, and protein structure prediction.

Chungong Yu is a Master, a senior engineer at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, China. His main research interests encompass bioinfomatics and protein structure prediction.

Dongbo Bu is a PhD, a Professor, and a PhD supervisor at State Key Lab of Processor, Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, and Central China Institute of Artificial Intelligence, China. His main research interests encompass algorithm design, bioinformatics, protein structure prediction, and deep learning.

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A Survey on Deep Learning-Based Algorithms for the Traveling Salesman Problem

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Sui, J., Ding, S., Huang, X. et al. A survey on deep learning-based algorithms for the traveling salesman problem. Front. Comput. Sci. 19, 196322 (2025). https://doi.org/10.1007/s11704-024-40490-y

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  • Received: 14 May 2024

  • Accepted: 20 June 2024

  • Published: 12 December 2024

  • DOI: https://doi.org/10.1007/s11704-024-40490-y

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Keywords

  • traveling salesman problem
  • algorithms design
  • deep learning
  • neural network
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