ASDSApplied Statistics and Data Science3066-84333066-8441Art and Technology10.61369/ASDS.2026060011Article基于图神经网络和多目标粒子群优化的智能路径规划方法及其应用https://artdesignp.com/journal/ASDS/2/6/10.61369/ASDS.2026060011吴荣康,吴伟佳,黄惠婷2026262026-06-20随着智能交通以及旅游规划的不断发展，对有限时间、预算的约束条件下优化旅行路线、提高赏花效益的问题越来越突出。为了克服与假日赏花旅游有关的城市访问和路线规划难题，本文提出了将图神经网络（GNN）和多目标粒子群优化（MOPSO）结合起来的智能路线规划方法（GNN-MOPSO）。该方法用GNN 从城市网络中提取结构特征，用MOPSO 优化多个目标，从而达到同时最大化赏花价值、最小化旅行成本的目的。从实验结果可以看出，在处理大规模城市网络的时候，GNN-MOPSO 比传统的PSO、NSGA-II 等方法在解决方案质量和多样性上要好。另外GNNMOPSO算法可以很好地处理复杂的约束，具有在智能交通、旅游路线优化等方面的应用前景。最后，本文对未来的几个研究方向进行了论述，即强化学习的集成以及算法在更大的城市网络中是否可以推广。图神经网络,多目标粒子群优化,路径规划,赏花,智能旅游

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