This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have ...This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.展开更多
针对当前网络入侵检测中的数据量较大、数据维度较高的特点,将飞蛾扑火优化(MFO)算法应用于网络入侵检测的特征选择中。鉴于MFO算法收敛过快、易陷入局部最优的问题,提出一种融合粒子群优化(PSO)的二进制飞蛾扑火优化(BPMFO)算法。该算...针对当前网络入侵检测中的数据量较大、数据维度较高的特点,将飞蛾扑火优化(MFO)算法应用于网络入侵检测的特征选择中。鉴于MFO算法收敛过快、易陷入局部最优的问题,提出一种融合粒子群优化(PSO)的二进制飞蛾扑火优化(BPMFO)算法。该算法引入MFO螺旋飞行公式,具有较强的局部搜索能力;结合了粒子群优化(PSO)算法的速度更新方法,让种群个体随着全局最优解和历史最优解的方向移动,增强算法的全局收敛性,从而避免易陷入局部最优。仿真实验以KDD CUP 99数据集为实验基础,分别采用支持向量机(SVM)、K最近邻(KNN)算法和朴素贝叶斯(NBC)3种分类器,与二进制飞蛾扑火优化(BMFO)算法、二进制粒子群优化(BPSO)算法、二进制遗传算法(BGA)、二进制灰狼优化(BGWO)算法和二进制布谷鸟搜索(BCS)算法进行了实验对比。实验结果表明,BPMFO算法应用于网络入侵检测的特征选择时,在算法精度、运行效率、稳定性、收敛速度以及跳出局部最优的综合性能上具有明显优势。展开更多
文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.
文摘针对当前网络入侵检测中的数据量较大、数据维度较高的特点,将飞蛾扑火优化(MFO)算法应用于网络入侵检测的特征选择中。鉴于MFO算法收敛过快、易陷入局部最优的问题,提出一种融合粒子群优化(PSO)的二进制飞蛾扑火优化(BPMFO)算法。该算法引入MFO螺旋飞行公式,具有较强的局部搜索能力;结合了粒子群优化(PSO)算法的速度更新方法,让种群个体随着全局最优解和历史最优解的方向移动,增强算法的全局收敛性,从而避免易陷入局部最优。仿真实验以KDD CUP 99数据集为实验基础,分别采用支持向量机(SVM)、K最近邻(KNN)算法和朴素贝叶斯(NBC)3种分类器,与二进制飞蛾扑火优化(BMFO)算法、二进制粒子群优化(BPSO)算法、二进制遗传算法(BGA)、二进制灰狼优化(BGWO)算法和二进制布谷鸟搜索(BCS)算法进行了实验对比。实验结果表明,BPMFO算法应用于网络入侵检测的特征选择时,在算法精度、运行效率、稳定性、收敛速度以及跳出局部最优的综合性能上具有明显优势。
