CL4CO: A Curriculum Training Framework for Graph-Based Neural Combinatorial Optimization

Yang Liu, Chuan Zhou, Peng Zhang, Zhao Li, Shuai Zhang, Xixun Lin, Xindong Wu

Abstract: Methods based on graph neural networks for solving combinatorial optimization (CO) problems have exhibited promising results in tackling a range of NP-hard problems, eliminating the necessity for reliance on manually created domain knowledge. Existing models including reinforcement learning (RL) framework assume that combinatorial instances in the training set contribute equally during training. Nevertheless, there is considerable variation in the quality of training instances, and the performance of models may suffer from the inclusion of low-quality training instances. This paper expands the current scope of neural solvers for CO problems through the incorporation of curriculum learning (CL). To alleviate the adverse impact of low-quality training instances, we propose CL4CO which utilizes CL strategy, a selective training method, to train models based on the rank of instances' quality in neural Combinatorial Optimization framework. Also, we introduce several candidate topology-aware metrics based on heterophily ratio and evaluation of clustering for the training scheduler. Furthermore, it is noteworthy to emphasize that it has potential to enhance the generalization capacity of RL-based baselines and we give a experimental validation. This enhancement plugin from the fact that CL empowers the acquired RL-based solver to effectively leverage commonly shared features within the same class of CO. Empirically, we conduct a case study on MaxCut, a classical discrete Oil-vector CO, to verify our findings and our results demonstrate that CL4CO is efficient and superiority with good generalization ability.

External IDs:dblp:conf/icdm/Liu000ZL024