Self-Improved Learning for Scalable Neural Combinatorial Optimization

arXiv:2403.19561v1 Announce Type: new
Abstract: The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design. However, existing methods struggle with large-scale problems, hindering their practical applicability. To overcome this limitation, this work proposes a novel Self-Improved Learning (SIL) method for better scalability of neural combinatorial optimization. Specifically, we develop an efficient self-improved mechanism that enables direct model training on large-scale problem instances without any labeled data. Powered by …

abstract arxiv cs.ai cs.lg design expert however novel optimization performance practical scalable scale shows struggle the end type work