为改善440C不锈钢航空精密液压滑阀零件的加工质量和效率,开展了基于响应曲面法(response surface methodology,RSM)和多目标灰狼优化算法(multi-objective grey wolf optimizer,MOGWO)的磨削工艺参数优化研究。利用响应曲面法分别建立...为改善440C不锈钢航空精密液压滑阀零件的加工质量和效率,开展了基于响应曲面法(response surface methodology,RSM)和多目标灰狼优化算法(multi-objective grey wolf optimizer,MOGWO)的磨削工艺参数优化研究。利用响应曲面法分别建立了表面粗糙度和圆柱度的回归模型;通过方差分析和响应曲面图分析,明确了磨削参数对试件表面粗糙度和圆柱度的交互影响;综合考虑加工质量和效率,采用MOGWO算法获得了多目标磨削工艺参数优化的Pareto解集,并结合层次分析法(analytic hierarchy process,AHP)和优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)得到了决策解。结果表明,在保证加工质量的前提下,材料去除率提升约20%,具有较高的工程应用价值。展开更多
软基水闸在水流侵蚀等因素作用下易发生底板脱空现象,极大地威胁水闸的安全运行.针对软基水闸底板脱空检测问题,提出一种基于门控循环单元(Gate Recurrent Unit, GRU)神经网络代理模型和多目标灰狼算法(Multi-Objective Grey Wolf Optim...软基水闸在水流侵蚀等因素作用下易发生底板脱空现象,极大地威胁水闸的安全运行.针对软基水闸底板脱空检测问题,提出一种基于门控循环单元(Gate Recurrent Unit, GRU)神经网络代理模型和多目标灰狼算法(Multi-Objective Grey Wolf Optimizer, MOGWO)的软基水闸底板脱空动力学反演方法.基于GRU神经网络构建表征软基水闸结构模态参数与脱空参数间非线性关系的数学代理模型,基于水闸结构固有频率、归一化振型建立软基水闸脱空参数反演的多目标优化函数,并采用MOGWO优化算法求解多目标优化问题的Pareto最优解.将所提方法应用于室内软基水闸物理模型两种脱空工况的反演计算.GRU神经网络代理模型精度优于多层前馈(Back Propagation, BP)神经网络代理模型及三阶多项式响应面模型,且反演脱空面积和模型实际脱空面积的相对误差分别为6.76%、5.58%,反演效果明显优于单目标反演方法.展开更多
Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation effi...Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.展开更多
The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO_(2)emissions during pavement construction and maintenance.Additionally,the laboratory m...The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO_(2)emissions during pavement construction and maintenance.Additionally,the laboratory mix design process,which involves selecting aggregate gradation and binder content,is time-consuming and labor-intensive.To accelerate the traditional mix design procedure,this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning(ML)and a meta-heuristic algorithm.Specifically,ML approaches were employed to model the relationship between volumetric properties(mixture bulk specific gravity(Gmb)and air void(VV))and both mixture component properties and mixture proportion,based on a dataset collected from literature with 660 mixture designs.Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization(MOGWO)algorithm,an automatic asphalt mix design was proposed to pursue three goals,including VV,cost,and CO_(2)emission.The results indicated that least squares support vector regression(LSSVR)and e Xtreme gradient boosting(XGBoost)achieved the highest prediction accuracies(correlation coefficient:0.92 for VV and 0.96 for Gmb).The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs.cost vs.CO_(2)emission.Compared to the traditional laboratory design,the optimal mixture with VV of4%achieves a cost saving of 2.46%and a reduction of 4.03%in carbon emission.The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.展开更多
文摘为改善440C不锈钢航空精密液压滑阀零件的加工质量和效率,开展了基于响应曲面法(response surface methodology,RSM)和多目标灰狼优化算法(multi-objective grey wolf optimizer,MOGWO)的磨削工艺参数优化研究。利用响应曲面法分别建立了表面粗糙度和圆柱度的回归模型;通过方差分析和响应曲面图分析,明确了磨削参数对试件表面粗糙度和圆柱度的交互影响;综合考虑加工质量和效率,采用MOGWO算法获得了多目标磨削工艺参数优化的Pareto解集,并结合层次分析法(analytic hierarchy process,AHP)和优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)得到了决策解。结果表明,在保证加工质量的前提下,材料去除率提升约20%,具有较高的工程应用价值。
文摘软基水闸在水流侵蚀等因素作用下易发生底板脱空现象,极大地威胁水闸的安全运行.针对软基水闸底板脱空检测问题,提出一种基于门控循环单元(Gate Recurrent Unit, GRU)神经网络代理模型和多目标灰狼算法(Multi-Objective Grey Wolf Optimizer, MOGWO)的软基水闸底板脱空动力学反演方法.基于GRU神经网络构建表征软基水闸结构模态参数与脱空参数间非线性关系的数学代理模型,基于水闸结构固有频率、归一化振型建立软基水闸脱空参数反演的多目标优化函数,并采用MOGWO优化算法求解多目标优化问题的Pareto最优解.将所提方法应用于室内软基水闸物理模型两种脱空工况的反演计算.GRU神经网络代理模型精度优于多层前馈(Back Propagation, BP)神经网络代理模型及三阶多项式响应面模型,且反演脱空面积和模型实际脱空面积的相对误差分别为6.76%、5.58%,反演效果明显优于单目标反演方法.
基金funded by the China Academy of Railway Sciences Corporation Limited Scientific Research Project(No:2023YJ080).
文摘Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.
基金sponsored by a grant from the Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems(CIAMTIS),a US Department of Transportation,University Transportation Center,United States,under federal grant number 69A3551847103。
文摘The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO_(2)emissions during pavement construction and maintenance.Additionally,the laboratory mix design process,which involves selecting aggregate gradation and binder content,is time-consuming and labor-intensive.To accelerate the traditional mix design procedure,this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning(ML)and a meta-heuristic algorithm.Specifically,ML approaches were employed to model the relationship between volumetric properties(mixture bulk specific gravity(Gmb)and air void(VV))and both mixture component properties and mixture proportion,based on a dataset collected from literature with 660 mixture designs.Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization(MOGWO)algorithm,an automatic asphalt mix design was proposed to pursue three goals,including VV,cost,and CO_(2)emission.The results indicated that least squares support vector regression(LSSVR)and e Xtreme gradient boosting(XGBoost)achieved the highest prediction accuracies(correlation coefficient:0.92 for VV and 0.96 for Gmb).The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs.cost vs.CO_(2)emission.Compared to the traditional laboratory design,the optimal mixture with VV of4%achieves a cost saving of 2.46%and a reduction of 4.03%in carbon emission.The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.
