Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver

TL;DR

This paper introduces an Instance-Conditioned Adaptation Model (ICAM) that significantly enhances the ability of neural routing solvers to generalize to large-scale combinatorial optimization problems like TSP, CVRP, and ATSP, with minimal additional computational cost.

Contribution

The work proposes a novel instance-conditioned adaptation module that improves large-scale generalization of neural routing solvers, addressing a key limitation of existing neural combinatorial optimization methods.

Findings

Achieves promising results on large-scale TSP, CVRP, and ATSP instances.

Provides fast inference times suitable for real-world applications.

Demonstrates improved generalization with low computational overhead.

Abstract

The neural combinatorial optimization (NCO) method has shown great potential for solving routing problems of intelligent transportation systems without requiring expert knowledge. However, existing constructive NCO methods still struggle to solve large-scale instances, which significantly limits their application prospects. To address these crucial shortcomings, this work proposes a novel Instance-Conditioned Adaptation Model (ICAM) for better large-scale generalization of neural routing solvers. In particular, we design a simple yet efficient instance-conditioned adaptation function to significantly improve the generalization performance of existing NCO models with a small time and memory overhead. In addition, with a systematic investigation on the performance of information incorporation between different attention mechanisms, we further propose a powerful yet low-complexity instance-conditioned adaptation module to generate better solutions for instances across different scales. Extensive experimental results on both synthetic and benchmark instances show that our proposed method is capable of obtaining promising results with a very fast inference time in solving large-scale Traveling Salesman Problems (TSPs), Capacitated Vehicle Routing Problems (CVRPs), and Asymmetric Traveling Salesman Problems (ATSPs). Our code is available at https://github.com/CIAM-Group/ICAM.