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HTS-LB:超图树搜索学习分支的学习分支

Yige Zhang1, Xiaoyan Zhang2, Jian Sun1

  • 1Ministry of Education Key Laboratory of NSLSCS, School of Computer and Electronic Information, Nanjing Normal University, Nanjing, 210023, China.

Neural networks : the official journal of the International Neural Network Society
|July 5, 2025
PubMed
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此摘要是机器生成的。

本研究引入了一种新的超图树搜索框架,用于学习分支 (HTS-LB),以改进基于机器学习的混合整数线性编程 (MILP) 解决. 对于复杂的优化问题,HTS-LB提高了可扩展性,信息丰富性和分支精度.

关键词:
组合优化的优化.超图神经网络的神经网络.机器学习 机器学习混合整数线性编程 混合整数线性编程

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科学领域:

  • 组合优化的优化.
  • 机器学习 机器学习
  • 运营研究 运营研究

背景情况:

  • 混合整数线性编程 (MILP) 对于资源有限的问题至关重要.
  • 现有的机器学习方法用于MILP解决,在可扩展性,信息丰富性和分支准确性方面面临挑战.
  • 目前的方法通常将MILP表示为双边图,从而限制了它们的有效性.

研究的目的:

  • 提出一个新的框架,超图树搜索学习分支 (HTS-LB),以解决基于机器学习的MILP解决的局限性.
  • 为了提高MILP解决方案的可扩展性,信息丰富性和分支精度.
  • 改善决策过程,解决复杂的优化问题.

主要方法:

  • 使用超图表来表示MILP,以提高可扩展性.
  • 为准确的分支政策编码开发一个超图注意网络 (HAN).
  • 实施树搜索门机制,以捕获用于变量表示更新的动态信息.

主要成果:

  • 在NP-hard MILP问题上,HTS-LB比流行的机器学习算法表现优越.
  • 该框架实现了更高的分支精度,更少的分支和绑定节点,以及更小的双原点间隙.
  • 集成到SCIP解决器中显示了大规模MILP的强大泛化.

结论:

  • 拟议的HTS-LB框架有效地解决了基于机器学习的MILP解决的关键挑战.
  • 在复杂的优化任务中,HTS-LB可显著提高效率和准确性.
  • 这种方法显示了通过机器学习集成推进组合优化领域的前景.