用视觉编码+分组偏好优化，高效求解大规模图路径问题
VAGPO: Vision-augmented Asymmetric Group Preference Optimization for Graph Routing Problems
- 结合视觉编码与注意力模型捕捉图结构和时序特征
- 异构分组偏好策略使收敛速度显著快于传统强化学习方法
- 无需重训练即可推广至1000节点的大规模实例，适合实际网络路由
图路由问题在互联网相关网络中至关重要，寻找最优路径对高效数据传输与内容分发至关重要。经典问题如旅行商问题（TSP）和带容量约束的车辆路径问题（CVRP）代表了基础的图优化挑战。尽管数据驱动优化方法已取得进展，但普遍存在训练效率低、难以泛化到大规模实例的问题。本文提出一种新型视觉增强型非对称分组偏好优化（VAGPO）方法。通过基于ResNet的视觉编码与基于Transformer的序列建模，VAGPO同时捕获图的空间结构与时间依赖性。进一步引入非对称分组偏好优化策略，显著加速收敛，优于常用策略梯度方法。在生成的TSP与CVRP实例及真实世界数据集上的实验表明，所提VAGPO方法获得具有竞争力的解质量。此外，该方法在未重新训练的情况下可有效泛化至最多1000个节点的大规模实例，展现出优异的学习效率与可扩展性。
原文摘要 · Abstract (English)
Graph routing problems play a vital role in web-related networks, where finding optimal paths across graphs is essential for efficient data transmission and content delivery. Classic routing formulations such as the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) represent fundamental graph optimization challenges. Recent data-driven optimization methods have made significant progress, yet they often face limitations in training efficiency and generalization to large-scale instances. In this paper, we propose a novel Vision-augmented Asymmetric Group Preference Optimization (VAGPO) approach. By leveraging ResNet-based visual encoding and Transformer-based sequential modeling, VAGPO captures both spatial structure and temporal dependencies. Furthermore, we introduce an asymmetric group preference optimization strategy that significantly accelerates convergence compared to commonly used policy gradient methods. Experimental results on generated TSP and CVRP instances, as well as real-world datasets, demonstrate that the proposed VAGPO approach achieves highly competitive solution quality. Additionally, VAGPO exhibits strong generalization to larger instances (up to 1000 nodes) without re-training, highlighting its effectiveness in both learning efficiency and scalability.
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