远距离意识重塑注意力,以增强神经解决器的泛化
概括
神经解决器因注意力得分分散而难以概括. 拟议的距离感知注意力重塑 (DAR) 方法可以在不添加参数的情况下改善路由问题的概括性.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 使用注意力机制的神经解决者 (NSs) 在路由问题上表现出色,例如旅行销售员问题 (TSP) 和车辆路由问题 (VRP).
- 现有的NS在泛化过程中表现出注意力得分分散,导致性能降低.
研究的目的:
- 为了增强神经解答器在路由问题上的概括能力.
- 解决神经网络解决器中注意力分数分散的问题.
主要方法:
- 提出了一种新的距离感知注意力重塑 (DAR) 方法.
- 使用节点间距离信息调整注意力得分,而不会增加神经网络参数.
- 旨在提高在较小数据集上训练的NS的能力,以解决更大,不同分布的问题.
主要成果:
- 在理论和经验上,DAR方法在改善NS概括方面表现出有效性.
- 广泛的实验表明,在各种路由问题上有优势:TSP,ATSP,CVRP,VRPTW,CARP和KP.
- 该方法使NS能够在大规模实例上做出更合理的选择.
结论:
- DAR是一种有效的技术,用于增强神经解决器在组合优化中的概括性能.
- 该方法提供了一个无参数的方法来改善神经网络中的注意力机制,以解决复杂的问题.
- 这项研究验证了DAR在广泛的NP难题中的实用性.
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