Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization

arXiv:2405.01906v1 Announce Type: cross
Abstract: The neural combinatorial optimization (NCO) approach has shown great potential for solving routing problems without the requirement of expert knowledge. However, existing constructive NCO methods cannot directly 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 combinatorial optimization. In particular, we design a powerful yet lightweight instance-conditioned adaptation module for the NCO model to generate …

abstract application arxiv cs.ai cs.lg expert however instance instances knowledge novel optimization prospects routing scale solve type work