论文标题
学会通过分配强大的优化解决路由问题
Learning to Solve Routing Problems via Distributionally Robust Optimization
论文作者
论文摘要
解决路由问题的最新深层模型始终假设训练节点的单个分布，从而严重损害了他们的跨分布概括能力。在本文中，我们利用群体分布强劲的优化（组DRO）来解决此问题，在训练过程中，我们共同优化了不同分布组的权重，并以交织方式以交织方式优化了深层模型的参数。我们还设计了一个基于卷积神经网络的模块，该模块允许深层模型在节点之间学习更多信息的潜在模式。我们在包括GCN和POMO在内的两种众所周知的深层模型上评估了所提出的方法。关于随机合成实例的实验结果以及来自两个基准数据集（即TSPLIB和CVRPLIB）的实验结果表明，我们的方法可以显着提高原始模型的跨分布通用性能。
Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally robust optimization (group DRO) to tackle this issue, where we jointly optimize the weights for different groups of distributions and the parameters for the deep model in an interleaved manner during training. We also design a module based on convolutional neural network, which allows the deep model to learn more informative latent pattern among the nodes. We evaluate the proposed approach on two types of well-known deep models including GCN and POMO. The experimental results on the randomly synthesized instances and the ones from two benchmark dataset (i.e., TSPLib and CVRPLib) demonstrate that our approach could significantly improve the cross-distribution generalization performance over the original models.
