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Test-Time Augmentation for Traveling Salesperson Problem

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Artificial Neural Networks and Machine Learning – ICANN 2024 (ICANN 2024)

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Abstract

We propose Test-Time Augmentation (TTA) as an effective technique for addressing combinatorial optimization problems, including the Traveling Salesperson Problem. In general, deep learning models possessing the property of invariance, where the output is uniquely determined regardless of the node indices, have been proposed to learn graph structures efficiently. In contrast, we interpret the permutation of node indices, which exchanges the elements of the distance matrix, as a TTA scheme. The results demonstrate that our method is capable of obtaining shorter solutions than the latest models. Furthermore, we show that the probability of finding a solution closer to an exact solution increases depending on the augmentation size.

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Notes

  1. 1.

    This paper assumes “Euclidean” TSP unless otherwise mentioned.

  2. 2.

    https://github.com/xbresson/TSP_Transformer.

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Acknowledgments

This work was supported by JST-JPMJAX23CR and JSPS-JP23KJ1723, JP21K18312, JP22H05172, JP22H05173 and JP24K22308.

Author information

Authors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Ryo Ishiyama, Takahiro Shirakawa, Seiichi Uchida & Shinnosuke Matsuo

Authors
  1. Ryo Ishiyama
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  2. Takahiro Shirakawa
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  3. Seiichi Uchida
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  4. Shinnosuke Matsuo
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Corresponding author

Correspondence to Ryo Ishiyama .

Editor information

Editors and Affiliations

  1. IDSIA USI-SUPSI, Lugano, Switzerland

    Michael Wand

  2. Comenius University, Bratislava, Slovakia

    Kristína Malinovská

  3. KAUST Center of Generative AI, Thuwal, Saudi Arabia

    Jürgen Schmidhuber

  4. Helmholtz Zentrum München, Neuherberg, Germany

    Igor V. Tetko

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Ishiyama, R., Shirakawa, T., Uchida, S., Matsuo, S. (2024). Test-Time Augmentation for Traveling Salesperson Problem. In: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15016. Springer, Cham. https://doi.org/10.1007/978-3-031-72332-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-72332-2_14

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