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

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)-ba...

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Vydané v:Frontiers of Computer Science Ročník 19; číslo 6; s. 196322
Hlavní autori: SUI, Jingyan, DING, Shizhe, HUANG, Xulin, YU, Yue, LIU, Ruizhi, XIA, Boyang, DING, Zhenxin, XU, Liming, ZHANG, Haicang, YU, Chungong, BU, Dongbo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Beijing Higher Education Press 01.06.2025
Springer Nature B.V
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ISSN:2095-2228, 2095-2236
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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.
AbstractList 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.
ArticleNumber 196322
Author DING, Zhenxin
YU, Chungong
BU, Dongbo
HUANG, Xulin
ZHANG, Haicang
LIU, Ruizhi
SUI, Jingyan
YU, Yue
XIA, Boyang
XU, Liming
DING, Shizhe
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  organization: Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450002, China
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  givenname: Yue
  surname: YU
  fullname: YU, Yue
  organization: Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
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  organization: University of Chinese Academy of Sciences, Beijing 100190, China
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  surname: ZHANG
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  organization: University of Chinese Academy of Sciences, Beijing 100190, China
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  organization: University of Chinese Academy of Sciences, Beijing 100190, China
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  surname: BU
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  email: dbu@ict.ac.cn
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Keywords deep learning
traveling salesman problem
algorithms design
neural network
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deep learning
traveling salesman problem
algorithms design
Document accepted on :2024-06-20
neural network
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Snippet This paper presents an overview of deep learning (DL)-based algorithms designed for solving the traveling salesman problem (TSP), categorizing them into four...
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SubjectTerms Algorithms
algorithms design
Computer Science
Deep learning
Heuristic methods
Large language models
Machine learning
neural network
Neural networks
Review Article
Traveling salesman problem
Title A survey on deep learning-based algorithms for the traveling salesman problem
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