首页|麻雀搜索算法优化极端梯度提升模型的岩石爆破块度预测

麻雀搜索算法优化极端梯度提升模型的岩石爆破块度预测

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为进一步提高岩石爆破块度预测效果,利用多个矿山的岩石爆破统计数据,通过优化极端梯度提升模型(extreme gradient boosting,XGBoost)超参数,建立一种基于随机森林(random forest,RF)特征选择的麻雀搜索算法(sparrow search algo-rithm,SSA)优化XGBoost爆破块度预测模型.利用麻雀搜索算法对XGBoost模型决策树数量、决策树最大深度、学习率3个核心超参数进行优化以提高运行效率;利用随机森林对输入特征进行筛选,并将优化后的特征集输入预测模型.结果表明:经特征集优化的模型,爆破块度预测效果整体上更加逼近实际值,且预测结果的可决系数(R-squared,R2)、均方根误差(root mean square error,RMSE)和平均绝对误差(mean absolute error,MAE)分别为 0.954、0.026 和 0.020,相较于 BP(back propaga-tion)神经网络、随机森林和XGBoost模型的效果更优,在实际应用中更具适用性,能为爆破参数设计和优化提供借鉴.
Prediction for Blasting Fragmentation of Rocks Using Extreme Gradient Boosting Optimized by Sparrow Search Algorithm
In order to further improve the predictive effect of rock blasting fragmentation,the blasting statistical data of several mines was used to build a prediction model,which was based on feature selection by random forest and the XGBoost regression prediction model optimized by the sparrow search algorithm.Aiming at improving the operating efficiency of the XGBoost regression prediction model,the sparrow search algorithm(SSA)was used to optimize their three core hyperparameters,including the number trees,the max depth and the learning rate.The input features selected by random forest were input into the model.The prediction effect of blasting fragmentation is closer to the actual value,and the R-squared(R2),the root mean square error(RMSE)and the mean absolute error(MAE)of the prediction results are 0.954,0.026 and 0.020.Compared with the back propagation(BP)neural network,the random forest and the XGBoost model,the proposed model is better and more applicable.It is concluded that the proposed model is more adaptive in practical application,and can provide reference for the design and optimization of blasting parameters.

sparrow search algorithm(SSA)XGBoost modelblasting fragmentationprediction

张朋超、赵有明、刘翔、廖黄正、何秋芝、易泽邦

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广西科技大学经济与管理学院,柳州 545006

柳州威宇爆破工程有限责任公司,柳州 545002

广西工业高质量发展研究中心,柳州 545006

桂林理工大学地球科学学院,桂林 541004

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麻雀搜索算法(SSA) XGBoost模型 爆破块度 预测

国家自然科学基金青年科学基金广西科技计划项目广西科技计划项目广西应急管理联合创新科技攻关项目广西科技大学博士基金项目广西科技大学博士基金项目企业委托产学研合作项目广西壮族自治区大学生创新创业训练计划项目

42003066桂科AD21220109桂科AD212201472024GXYJ011校科博20S1021Z29WQHG-KJXX-2022-018S202310594103

2024

10.12404/j.issn.1671-1815.2306182

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(24)