基于自适应多策略群优化算法的车辆路径低碳规划模型的设计
Qi Guo1, Rui Li2, Changjiang Zheng3
1Department of Automotive Engineering, Anhui Institute of Automotive Technology, Hefei, Anhui, China.
PeerJ. Computer science
|March 10, 2025
概括
本研究介绍了一种适应合作图形神经网络 (ACGNN),用于低碳车辆路线规划. 通过优化复杂的路线,ACGNN提高了运输效率和可持续性,优于传统方法.
科学领域:
- 运输工程 运输工程
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 越来越多的车辆复杂性和数量需要先进的路线规划.
- 交通拥堵,复杂的路线和高能耗降低了效率,增加了污染.
- 实现低碳运输需要战略路线优化.
研究的目的:
- 提出一种新的低碳车辆路径规划模型.
- 解决运输效率和环境可持续性的挑战.
- 开发一个集成图形神经网络和群优化的模型.
主要方法:
- 开发了一个自适应合作图形神经网络 (ACGNN) 模型.
- 作为输入,利用了道路网络和历史轨迹的图形数据.
- 使用子图选用于数据质量,GNN用于节点/边缘优化,以及殖民地优化 (ACO) 用于全球路径.
主要成果:
- 与迪克斯特拉的,RGN和传统的GNN相比,ACGNN展示了优越的路径规划性能.
- 在定制数据集比较中,ACO显著优于模拟化 (SA) 和粒子群集优化 (PSO).
- 该模型在路径规划结果方面取得了显著的改进.
结论:
- ACGNN为车辆路径规划提供了一种创新的技术解决方案.
- 拟议的方法有效地提高了运输效率.
- ACGNN有助于实现低碳和环境可持续的运输目标.
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