Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs

ICLR 2026 Conference Submission18127 Authors

Keywords: Combinatorial Optimization, Reinforcement Learning, Graph-based Machine Learning, Multigraphs, Traveling Salesman Problem, Multi-Objective Optimization

TL;DR: We introduce two GNN-based models for routing with multiple objectives on multigraphs and asymmetric graphs

Abstract: Learning-based methods for routing have gained significant attention in recent years, both in single-objective and multi-objective contexts. Yet, existing methods are unsuitable for routing on multigraphs, which feature multiple edges with distinct attributes between node pairs, despite their strong relevance in real-world scenarios. In this paper, we propose two graph neural network-based methods to address multi-objective routing on multigraphs. Our first approach operates directly on the multigraph by autoregressively selecting edges until a tour is completed. The second model, which is more scalable, first simplifies the multigraph via a learned pruning strategy and then performs autoregressive routing on the resulting simple graph. We evaluate both models empirically, across a wide range of problems and graph distributions, and demonstrate their competitive performance compared to strong heuristics and neural baselines.

Primary Area: learning on graphs and other geometries & topologies

Submission Number: 18127