一个基于图形神经网络的解决方案框架,用于图形优化问题
概括
本研究介绍了一种使用香农的新型解决器框架,用于在约束满足问题 (CSP) 和组合优化问题 (COP) 中分支启发式. 该方法平衡了一般性和特异性,在NP难题上取得了竞争力的结果.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 约束满足问题 (CSP) 和组合优化问题 (COP) 经常使用回溯与分支启发式来解决.
- 现有的分支启发式要么是特定于问题而复杂的,要么是一般的,但潜在的低于最佳的.
- 存在需要的启发式,平衡一般性与问题特定的效率.
研究的目的:
- 引入一个解决器框架,将Shannon积集成到CSP和COP的分支启发式.
- 开发一种方法,使回溯能够在减少不确定性的情况下解决问题.
- 学习使用图形神经网络 (GNN) 进行分支的概率分布.
主要方法:
- 开发了一个使用香农来分支启发学的解决器框架.
- 使用图形神经网络 (GNN) 模型来学习概率分布.
- 利用了从GNN训练的概率方法中得出的损失函数.
主要成果:
- 该框架应用于NP-hard (最小) 主导集团和边缘集团覆盖问题.
- 与最先进的解决方案相比,取得了具有竞争力的结果.
- 对于主导集团问题来说,已经证明了分支的减少,对于边缘集团覆盖问题来说,更小的边缘集团覆盖 (ECCs).
结论:
- 提出的基于香农的分支启发式启发框架有效地弥合了一般和特定启发式启发式之间的差距.
- 基于GNN的方法为学习有效的分支策略提供了一种实用的方法.
- 该框架显示了提高复杂的NP-hard优化问题的性能的前景.
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