GAMA用图注意力机制提升路径规划的决策能力。
GAMA: A Neural Neighborhood Search Method with Graph-aware Multi-modal Attention for Vehicle Routing Problem
- 将问题实例与解状态分模态编码，用图神经网络建模
- 在多种测试集上优于现有神经基线方法
- 适合需要高效路径优化的智能交通系统
神经邻域搜索方法在求解车辆路径问题（VRP）方面展现出潜力。然而，现有方法多依赖简化的状态表示，并通过简单拼接融合异构信息，难以捕捉丰富的结构与语义上下文。为此，本文提出GAMA，一种基于图感知多模态注意力的神经邻域搜索方法。GAMA利用图神经网络分别编码问题实例及其演化解作为不同模态，并通过堆叠的自注意力与交叉注意力层建模其内部与跨模态交互。门控融合机制进一步将多模态表示整合为结构化状态，使策略能做出更明智且可泛化的操作选择。在多种合成与基准实例上的大量实验表明，GAMA显著优于近期神经基线方法。进一步的消融研究证实，多模态注意力机制与门控融合设计对性能提升起关键作用。
原文摘要 · Abstract (English)
Recent advances in neural neighborhood search methods have shown potential in tackling Vehicle Routing Problems (VRPs). However, most existing approaches rely on simplistic state representations and fuse heterogeneous information via naive concatenation, limiting their ability to capture rich structural and semantic context. To address these limitations, we propose GAMA, a neural neighborhood search method with Graph-aware Multi-modal Attention model in VRP. GAMA encodes the problem instance and its evolving solution as distinct modalities using graph neural networks, and models their intra- and inter-modal interactions through stacked self- and cross-attention layers. A gated fusion mechanism further integrates the multi-modal representations into a structured state, enabling the policy to make informed and generalizable operator selection decisions. Extensive experiments conducted across various synthetic and benchmark instances demonstrate that the proposed algorithm GAMA significantly outperforms the recent neural baselines. Further ablation studies confirm that both the multi-modal attention mechanism and the gated fusion design play a key role in achieving the observed performance gains.
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