Work

Distance-Aware Attention Reshaping for Enhancing Generalization of Neural Solvers

Year: 2025

Type: article

Abstract: Neural solvers (NSs) based on the attention mechanism have demonstrated remarkable effectiveness in solving routing problems like traveling salesman problems (TSPs) and vehicle routing problems (VRPs). However, in the generalization process, we find a phenomenon of the dispersion of attention scores in existing NSs, which leads to poor performance. To improve the generalization ability of NSs, this article proposes a distance-aware attention reshaping (DAR) method. Specifically, without increasing any parameter of the neural network (NN), we utilize the distance information between nodes to adjust attention scores. This enables an NS trained on small-scale instances with a certain distribution to make rational choices when solving large-scale problems with different distributions. Its effectiveness is verified both theoretically and empirically. Extensive experiments on the TSP, asymmetric TSP (ATSP), capacitated VRP (CVRP), VRP with time windows (VRPTW), capacitated arc routing problem (CARP), and knapsack problem (KP) demonstrate the advantages of our method. Our code is available at https://github.com/ftwangyang/DAR.

Source: IEEE Transactions on Neural Networks and Learning Systems

DOI: 10.1109/tnnls.2025.3588209

Authors: Yang Wang, Ya-Hui Jia, Wei-Neng Chen, Yi Mei

Institutions: South China University of Technology, Victoria University of Wellington

Language: English

Topic: Vehicle Routing Optimization Methods

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province

Awards: G5547941978, G7994208271