面向旅行商问题的测试时增强
Test-Time Augmentation for Traveling Salesperson Problem
AI总结：
针对旅行商等组合优化问题，该研究将节点索引排列设计为测试时增强方案，在不依赖不变性建模的情况下获得比最新模型更短的路径，并提升逼近精确解的概率。
摘要：
我们提出将测试时增强（Test-Time Augmentation，TTA）作为一种有效技术，用于求解包括旅行商问题（Traveling Salesperson Problem）在内的组合优化问题。通常，具有不变性（即无论节点索引如何，输出都被唯一确定）的深度学习模型已被提出，用于高效学习图结构。与之不同，我们将交换距离矩阵元素的节点索引排列解释为一种 TTA 方案。结果表明，与最新模型相比，我们的方法能够获得更短的解。此外，我们还表明，找到更接近精确解的解的概率会随增强规模的增加而提高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
