使用XGBoost-DRWIACO框架高效地优化大型钻机的驱动参数:用于增加料速度
Hao Guo1, Lin Lin1, Jinlei Wu1
1School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究引入了XGBoost-DRWIACO框架,以优化大型钻孔参数,增加料速度并将道建设成本降低19%. 该方法在复杂的道工程环境中提高了可靠性和效率.
科学领域:
- 道工程 道工程是指道工程.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 大型钻机在道工程中至关重要,料速度的优化会影响施工成本.
- 现有的优化方法在复杂的道条件下缺乏准确性和效率.
- 由于道施工中的风险,高可靠性和效率是必不可少的.
研究的目的:
- 为大型钻机提出一种新,准确和高效的驾驶参数优化方法.
- 解决道工程当前优化算法的局限性.
- 通过改进料速度优化来降低道建设成本.
主要方法:
- 开发了一个基于数据的料速度预测模型,使用XGBoost作为评估函数.
- 提出了一种改进的殖民地算法 (DRWIACO),在代策略的同时减少维度.
- 将XGBoost和DRWIACO集成到一个统一的参数优化框架中.
主要成果:
- 拟议的XGBoost-DRWIACO框架实现了低于10%的错误率.
- 与现有方法相比,效率提高了30%以上.
- 与实际料速度相比,道建设成本减少了19%.
结论:
- 在XGBoost-DRWIACO框架提供高精度和高效率的大型钻孔参数优化.
- 该方法符合道工程的严格可靠性和效率要求.
- 通过优化建筑参数,可以实现显著的经济效益.
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