大邻里搜索和超-启发式对于有能力的p-中位数问题
Ida Gjergji1, Lucas Kletzander2, Nysret Musliu2
1DBAI, TU Wien, Favoritenstrasse 11, Vienna, 1040 Austria.
Journal of heuristics
|January 28, 2026
概括
这项研究引入了大邻里搜索 (LNS) 和超启发式对能力化p-中位数问题 (CPMP). 这些方法改进了现有的方法,为地理规划问题提供了更好的解决方案和更低的GAP值.
科学领域:
- 运营研究 运营研究
- 计算优化计算优化
- 应用数学 应用数学 应用数学
背景情况:
- 有能力的p-中位数问题 (CPMP) 对于位置规划至关重要,影响城市规划和医疗设施的位置.
- 有效地解决CPMP对于优化各种现实场景中的资源配置至关重要.
研究的目的:
- 开发和评估新型的大邻里搜索 (LNS) 算法和超启发式用于容量化p-中位数问题 (CPMP).
- 将拟议方法的性能与CPMP的最先进方法进行比较.
主要方法:
- 实施一个大型邻里搜索 (LNS) 框架,与多种破坏操作员以及修复阶段的确切解决者合作.
- 开发和应用超启发式学习,利用问题独立的策略,为CPMP生成低级启发式学习.
- 在各种CPMP实例上对LNS,超启发学和现有的最先进方法进行比较分析.
主要成果:
- 与当前最先进的方法相比,拟议的LNS和超启发式实现了较低的平均GAP值.
- 开发的算法为几个CPMP实例找到了改进的解决方案.
- 详细的分析表明,超启发学在具有不同结构的实例中具有强大的性能.
结论:
- 超启发学显示了能力化p-中位数问题 (CPMP) 的强大性能,可以适应相似的优化场景,只需最小的修改.
- 这项研究证实了LNS和超启发学在解决复杂的地理规划问题的解决方案方面的有效性.
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