神经改进 图形组合优化问题的神经改进启发式
IEEE transactions on neural networks and learning systems
|October 4, 2023
概括
一个新的神经改进 (NI) 模型增强了图形神经网络 (GNN) 的组合优化 (CO). 这个模型有效地处理边缘和节点信息,显著提高了复杂图形问题的性能.
科学领域:
- 图形神经网络 图形神经网络
- 组合优化的优化.
- 机器学习 机器学习
背景情况:
- 图形神经网络 (GNN) 架构和计算能力的近期进展显著影响了组合优化 (CO).
- 神经改善 (NI) 模型对于CO是成功的,但仅限于基于节点特性的问题,不包括边缘编码的信息.
- 现有的NI模型与基于图形的问题作斗争,其中关键数据位于边缘,而不仅仅是节点.
研究的目的:
- 为基于图形的组合优化问题引入一种新的神经改善 (NI) 模型.
- 开发一个NI模型,能够利用节点,边缘或两者的编码信息.
- 通过指导邻里操作选择来增强登算法.
主要方法:
- 开发了一个用于图形神经网络 (GNN) 的新型神经改善 (NI) 模型.
- 在NI框架内集成节点和边缘信息处理.
- 应用该模型作为登算法的组件,用于组合优化.
主要成果:
- 拟议的NI模型在偏好排名问题 (PRP) 中实现了99百分点的表现.
- 在推邻里操作方面,在传统方法上表现出优越的表现.
- 在旅行销售员问题上达到98个百分点,在图形分区问题 (GPP) 上达到97个百分点.
结论:
- 新的NI模型有效地处理基于图形的问题与节点和/或边缘信息.
- 这种方法显著提高了组合优化任务的性能.
- 该模型为PRP,TSP和GPP等问题提供了多功能解决方案.
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