通过模型架构的镜头来改善神经车辆路由的概括问题解决者
Yubin Xiao1, Di Wang2, Xuan Wu1
1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
概括
对于车辆路由问题 (VRPs) 的神经模型在概括方面存在困难. 本研究介绍了基于的缩放因子 (ESF) 和分布特异 (DS) 解码器,以改善无需高培训成本的概括性.
科学领域:
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 神经模型对车辆路由问题 (VRPs) 有希望,但往往缺乏概括能力.
- 现有的改善泛化的方法可能在计算上昂贵或缺乏模型灵活性.
研究的目的:
- 为了提高VRPs的神经模型的概括性.
- 引入新,计算效率高,可适应的建筑组件.
主要方法:
- 提出了一个基于插件和操作的基于的缩放因子 (ESF),通过调整注意力权重来改善尺寸概括.
- 引入了一个分布式特定 (DS) 解码器,通过明确建模各种VRP模式来增强分布式通用化.
- 将ESF和DS解码器集成到现有的神经网络架构中.
主要成果:
- 在各种VRP大小和分布的泛化方面取得了显著的改进.
- 在合成和现实世界的基准数据集上都表现出有效性.
- 展示了不同VRP变体的适用性,如旅行销售员问题和能力VRP.
结论:
- ESF和DS解码器是提高神经VRP模型概括性的有效和计算价格低廉的组件.
- 这些组件可以很容易地集成到现有策略中,以进一步提高性能.
- 提出的方法为提高神经VRP解决方案的稳定性提供了一种多功能解决方案.
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