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对于反顶集问题的动态值搜索.

Wen Sun1, Jin-Kao Hao2, Zihao Wu1

  • 1School of Cyber Science and Engineering, Southeast University, Nanjing, China.

PeerJ. Computer science
|June 22, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了NP-hard反顶集问题的新算法. 代的动态值搜索算法在基准图表上取得了最先进的结果,为大多数实例找到最佳解决方案.

关键词:
下降搜索下降搜索 搜索下降动态值搜索 动态值搜索反顶集是一组反顶集.启发式 启发式是一种启发式的启发式.

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科学领域:

  • 计算机科学 计算机科学
  • 图形理论 图形理论
  • 组合优化的优化.

背景情况:

  • 回应顶点集问题旨在找到一个最小的顶点子集,其去除使定向图成为非循环的.
  • 这个问题在计算上具有挑战性,因为它的NP-hard性质限制了实际应用.
  • 现有的算法很难有效地为大型或复杂的图形找到最佳解决方案.

研究的目的:

  • 开发一个有效的算法来解决NP-hard反顶集问题.
  • 为了提高现有的最先进的算法对这个问题的性能.
  • 分析关键算法组件对性能的影响.

主要方法:

  • 开发一个代的动态值搜索算法.
  • 集成本地优化,动态值搜索和扰动技术.
  • 对来自不同来源的101基准定向图进行了广泛的计算实验.

主要成果:

  • 拟议的算法在24个图表中实现了创纪录的最佳解决方案.
  • 它匹配了101个基准图表中的75个最知名的解决方案.
  • 与当前最先进的方法相比,该算法表现出卓越的性能.

结论:

  • 代的动态值搜索算法对反顶集问题非常有效.
  • 该算法在解决这个计算上具有挑战性的问题方面取得了重大进展.
  • 进一步的分析证实了其核心组件在实现最佳结果方面的有效性.