改进了动态编程方法,用于解决多目标和多阶段决策问题
Zhihao Liang1,2, Kegang Zhao2, Kunyang He2
1School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China.
Scientific reports
|January 11, 2025
概括
本研究介绍了非主导排序动态编程 (NSDP),这是一个用于复杂多目标问题的高效算法. NSDP提高了解决效率和解决方案的多样性,优于现有的方法.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 多目标和多阶段的决策问题是复杂的,涉及跨多个目标和高维的控制变量之间的权衡.
- 现有的智能优化算法通常对这些具有挑战性的问题具有较低的解决效率.
研究的目的:
- 提出一个高效的算法,非主导排序动态编程 (NSDP),用于多目标和多阶段的决策问题.
- 为了提高复杂决策场景的优化算法的解决效率和解决方案多样性.
主要方法:
- 将非主导分类纳入传统的动态编程.
- 整合两个快速的非主导分类方法.
- 在NSDP框架内利用基于动态拥挤距离的精英主义战略.
主要成果:
- 在12个基准测试函数上,NSDP算法在多个指标上表现出卓越的性能.
- 在一个多目标旅行销售员问题上的验证证实了NSDP的有效性.
- 与非主导排序基因算法II (NSGA-II) 和多目标粒子集群优化 (MOPSO) 相比,NSDP实现了更高的解决效率.
结论:
- 对于多目标和多阶段决策,NSDP在解决效率和解决方案多样性方面提供了显著的改进.
- 与NSGA-II和MOPSO等既定方法相比,提出的算法提供了更有效的方法.
- 对于复杂的决策问题,NSDP代表了智能优化的有前途的进步.
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