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

2024-05-03

arXiv.org

AI Summary

Existing neural combinatorial optimization (NCO) methods exhibit poor generalization to large-scale routing problems—such as the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP)—limiting their applicability in real-world intelligent transportation systems. To address this, we propose Instance-Conditional Adaptive Mechanism (ICAM), a construction-based graph neural network model that achieves cross-scale adaptability via lightweight adapters conditioned on instance-specific embeddings. We further introduce a novel three-stage unsupervised reinforcement learning paradigm, enabling end-to-end training on instances ranging from 100 to 1,000 nodes without access to optimal solution labels. Experiments demonstrate that ICAM achieves state-of-the-art performance among construction-based NCO approaches on TSP and CVRP benchmarks, scales robustly up to 1,000 nodes, and delivers highly efficient inference—significantly outperforming existing methods.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Improving large-scale generalization of neural routing solvers
• Reducing time and memory overhead in NCO methods
• Enhancing solution quality across different problem scales

Innovation

• Instance-Conditioned Adaptation Model (ICAM) for scalability
• Efficient adaptation function with low overhead
• Low-complexity module for multi-scale instance solutions