Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2022

Abstract

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 wellknown 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.

Keywords

Benchmark datasets, Convolutional neural network, Generalization ability, Generalization performance, Learn+, Module-based, Original model, Robust optimization, Routing problems, Synthesised

Discipline

Software Engineering | Theory and Algorithms

Publication

Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Virtual Online, Feb 22- Mar 1

Volume

36

First Page

9786

Last Page

9794

ISBN

9781577358763

Publisher

Association for the Advancement of Artificial Intelligence

City or Country

Virtual, Online

Copyright Owner and License

Authors

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