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.
@article{arxiv.2405.04767,
title = {Test-Time Augmentation for Traveling Salesperson Problem},
author = {Ryo Ishiyama and Takahiro Shirakawa and Seiichi Uchida and Shinnosuke Matsuo},
journal= {arXiv preprint arXiv:2405.04767},
year = {2024}
}