The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits.

中文翻译：

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利用 XGBoost-DRWIACO 框架高效优化台钻驱动参数：应用于提高进给速度

台车是隧道工程中常用的掘进设备。降低隧道施工成本的关键决策问题之一是优化主要驱动参数，提高巨型钻机的进给速度。隧道工程风险高、工况复杂，掘进参数的优化需要满足高可靠性、高效率的要求。现有驱动参数优化的优化算法的缺陷在于复杂工况下的评价函数精度不高、算法效率低。针对上述问题，提出一种基于XGBoost-DRWIACO框架的高精度、高效率的行驶参数优化方法。建立了基于XGBoost的数据驱动的进给速度预测模型作为评价函数，在复杂工况下具有较高的精度，保证了优化结果的高可靠性。同时，提出了一种基于迭代降维策略的改进蚁群算法（DRWIACO）。 DRWIACO旨在通过解决蚁群算法（ACO）迭代效率低下的问题来提高效率，其表现为陷入局部最优、收敛速度慢、收敛在某个维度上有轻微波动等问题。实验结果表明，与对比方法相比，该框架误差小于10%，效率提高30%以上，满足隧道施工高可靠性、高效率的要求。更重要的是，施工成本较实际进给速度降低19%，提高了经济效益。