一个基于变压器的结构意识模型来解决旅行销售员的问题
Chun-Sheng Zhao1, Li-Pei Wong1
1School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia.
PloS one
|April 7, 2025
概括
本研究介绍了一个结构意识的变压器模型,用于旅行销售员问题 (TSP). 通过结合图形特征,该模型实现了近乎最佳的解决方案,超过了现有的方法.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 计算机科学 计算机科学
背景情况:
- 组合优化问题 (COP) 从变压器架构中受益.
- 现有的变压器模型往往忽略了COP中的图形结构,例如旅行销售员问题 (TSP).
研究的目的:
- 开发一个基于变压器的模型,有效地利用图形结构特征来解决TSP.
- 为了增强变压器捕获TSP图形特征的能力.
主要方法:
- 在变压器编码器中引入了接近中心编码和空间编码.
- 集成了一个解码机制,强调开始,最近和所有先前访问的节点,以产生动态的旅游.
- 在模型培训中使用深度强化学习.
主要成果:
- 与康科德解决器相比,实现了较低的偏差率 (20节点的0.03%,50节点的0.16%,100节点TSP的1.13%)
- 超过了经典的启发式启发,OR工具和基于比较学习的方法.
- 证明了时间效率和解决方案质量之间的平衡.
结论:
- 提出的结构感知变压器模型有效地解决了TSP.
- 图形结构信息显著提高了TSP的深度神经网络性能.
- 该模型显示了在解决组合优化问题的可扩展性和有效性.
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