混合平衡GFlowNet求解车辆路径问题
Hybrid-Balance GFlowNet for Solving Vehicle Routing Problems
AI总结：
针对现有GFlowNet方法在VRP中忽视局部优化的问题，提出HBG框架，自适应整合TB与DB，并设计仓库中心推理策略，在CVRP和TSP上显著提升求解质量与泛化能力。
摘要：
现有的基于GFlowNet的车辆路径问题（VRP）求解方法通常采用轨迹平衡（TB）来实现全局优化，但往往忽略了局部优化的重要方面。虽然详细平衡（DB）能更有效地处理局部优化，但单独使用DB在求解VRP时表现不足，因为VRP本质上需要整体轨迹优化。为解决这些局限，我们提出了混合平衡GFlowNet（HBG）框架，该框架通过对齐TB和DB内在互补的优势，以原则性和自适应的方式独特地整合了二者。此外，我们针对以仓库为中心的场景（如容量受限车辆路径问题（CVRP））提出了一种专门的推理策略，利用仓库节点在选择后继节点时更大的灵活性。尽管具有这种专门性，HBG仍保持广泛的适用性，可有效扩展到没有显式仓库的问题，如旅行商问题（TSP）。我们通过将HBG集成到两个成熟的基于GFlowNet的求解器（即AGFN和GFACS）中进行评估，并在CVRP和TSP上均展示了持续且显著的改进，凸显了我们的方法所带来的更优解质量和泛化能力。
英文摘要：
Existing GFlowNet-based methods for vehicle routing problems (VRPs) typically employ Trajectory Balance (TB) to achieve global optimization but often neglect important aspects of local optimization. While Detailed Balance (DB) addresses local optimization more effectively, it alone falls short in solving VRPs, which inherently require holistic trajectory optimization. To address these limitations, we introduce the Hybrid-Balance GFlowNet (HBG) framework, which uniquely integrates TB and DB in a principled and adaptive manner by aligning their intrinsically complementary strengths. Additionally, we propose a specialized inference strategy for depot-centric scenarios like the Capacitated Vehicle Routing Problem (CVRP), leveraging the depot node's greater flexibility in selecting successors. Despite this specialization, HBG maintains broad applicability, extending effectively to problems without explicit depots, such as the Traveling Salesman Problem (TSP). We evaluate HBG by integrating it into two established GFlowNet-based solvers, i.e., AGFN and GFACS, and demonstrate consistent and significant improvements across both CVRP and TSP, underscoring the enhanced solution quality and generalization afforded by our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
