Q-learning 改进了金子优化算法,并将其应用于液压系统可靠性优化.
Dongning Chen1, Haowen Wang2, Dongbo Hu2
1School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China. dnchen@ysu.edu.cn.
Scientific reports
|October 19, 2024
概括
一个新的Q学习改进金优化 (QIGJO) 算法增强了智能运动和优化性能. 这种先进的方法提高了对汇率的准确性和对复杂的工程问题,包括液压系统可靠性的全球探索.
科学领域:
- 人工智能的人工智能
- 优化算法 优化算法
- 可靠性工程可靠性工程
背景情况:
- 黄金子优化 (GJO) 需要增强智能运动和性能.
- 现有的优化技术可能缺乏足够的全球勘探能力.
- 混凝土车中的液压系统的可靠性至关重要.
研究的目的:
- 提出Q学习改进金优化 (QIGJO) 算法.
- 为了提高GJO算法的性能和智能运动行为.
- 应用QIGJO来优化混凝土车中的液压系统的可靠性.
主要方法:
- 通过整合五个更新机制和一个双人群Q学习协作机制,开发了QIGJO.
- 引入了一种新的趋同因子,以提高GJO的趋同能力.
- 使用连续时间多维TS动态断层树 (CM-TSdFT) 建立了可靠性优化模型,考虑了运行时间和影响因素.
主要成果:
- 在23个基准函数和CEC2022上,QIGJO表现出卓越的表现.
- 该算法表现出高的收精度,并显著增强了全球探索.
- 使用QIGJO优化液压系统可靠性模型取得了很好的结果.
结论:
- 比起标准的GJO,QIGJO提供了显著的改进.
- 拟议的算法为复杂的优化任务提供了一个强大的方法.
- QIGJO为优化液压系统的可靠性提供了宝贵的方法支持.
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