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

• URL: http://arxiv.org/abs/2405.01906v1
• Date: Fri, 3 May 2024 08:00:19 GMT
• Title: Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization
• Authors: Changliang Zhou, Xi Lin, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang,
• Abstract summary: This work proposes a novel Instance-Conditioned Adaptation Model (ICAM) for better large-scale generalization of neural optimization. In particular, we design a powerful yet lightweight instance-conditioned Routing adaptation module for the NCO model. We develop an efficient three-stage reinforcement learning-based training scheme that enables the model to learn cross-scale features without any labeled optimal solution.
• Score: 15.842155380912002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 better solutions for instances across different scales. In addition, we develop an efficient three-stage reinforcement learning-based training scheme that enables the model to learn cross-scale features without any labeled optimal solution. Experimental results show that our proposed method is capable of obtaining excellent results with a very fast inference time in solving Traveling Salesman Problems (TSPs) and Capacitated Vehicle Routing Problems (CVRPs) across different scales. To the best of our knowledge, our model achieves state-of-the-art performance among all RL-based constructive methods for TSP and CVRP with up to 1,000 nodes.

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