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This study investigates a method to approximate solutions to the Traveling Salesman Problem (TSP), a combinatorial optimization problem, by representing it as an image and applying deep learning. U-Net was used as the deep learning model and applied to TSP instances with between 15 and 150 cities. The results showed that for cases with fewer cities, the approximate tour could be visually confirmed as roughly correct. However, for cases with more cities, local noise appeared, indicating the need for improvement. Additionally, the model showed overfitting to data with a larger number of points.

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A Solution of Traveling Salesman Problem Using Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Azuharu Fayyaz Nishida,
  • Takehiko Ogawa

摘要

This study investigates a method to approximate solutions to the Traveling Salesman Problem (TSP), a combinatorial optimization problem, by representing it as an image and applying deep learning. U-Net was used as the deep learning model and applied to TSP instances with between 15 and 150 cities. The results showed that for cases with fewer cities, the approximate tour could be visually confirmed as roughly correct. However, for cases with more cities, local noise appeared, indicating the need for improvement. Additionally, the model showed overfitting to data with a larger number of points.