提出协同训练框架，提升路径规划神经模型的抗干扰能力。
Collaboration! Towards Robust Neural Methods for Routing Problems
- 多模型协作对抗训练，增强鲁棒性
- 在基准测试上显著提升抗扰动性能
- 适合需要高可靠性的智能调度系统
尽管现有神经方法在车辆路径问题（VRP）中展现出高效与低依赖领域知识的优势，但其在带有精心构造扰动的干净实例上表现严重下降。为提升鲁棒性，我们提出一种基于集成的协同神经框架（CNF），用于防御神经VRP方法，该方向在文献中尚属空白。给定一个神经VRP方法，我们通过协作方式对抗性训练多个模型，以协同增强对攻击的抵抗能力，同时提升在干净实例上的泛化性能。设计了一种神经路由器，智能分配训练实例，改善负载均衡与协作效率。大量实验验证了CNF在多种攻击下对不同神经VRP方法的有效性与通用性。值得注意的是，该方法在基准实例上还实现了出色的分布外泛化能力。
原文摘要 · Abstract (English)
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues -- their performance significantly deteriorates on clean instances with crafted perturbations. To enhance robustness, we propose an ensemble-based Collaborative Neural Framework (CNF) w.r.t. the defense of neural VRP methods, which is crucial yet underexplored in the literature. Given a neural VRP method, we adversarially train multiple models in a collaborative manner to synergistically promote robustness against attacks, while boosting standard generalization on clean instances. A neural router is designed to adeptly distribute training instances among models, enhancing overall load balancing and collaborative efficacy. Extensive experiments verify the effectiveness and versatility of CNF in defending against various attacks across different neural VRP methods. Notably, our approach also achieves impressive out-of-distribution generalization on benchmark instances.
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