The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper pro...The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper proposes an MTS variable optimization method based on chaos quantum-behavior particle swarm.In order to avoid the influence of complex collinearity on the distance measurement results,the Gram-Schmidt orthogonalization method is first used to calculate the Mahalanobis distance(MD)value.Then,the optimal threshold point of the system classification is determined through the receiver operating characteristic(ROC)curve;the misclassification rate and the selected variables are defined;the multi-objective mixed programming model is built.The chaos quantum-behavior particle swarm optimization(CQPSO)algorithm is proposed to solve the optimization combination,and the algorithm performs binary coding on the particle based on probability.Using the optimized combination of variables,a new Mahalanobis-Taguchi metric based prediction system is established to complete the task of precise discrimination.Finally,a fault diagnosis for the steel plate is taken as an example.The experimental results show that the proposed method can effectively enhance the iterative speed and optimization precision of the particles,and the prediction accuracy of the optimized MTS is significantly improved.展开更多
使用量子粒子群优化算法(QPSO),将可能的Web服务工作流执行路径看作粒子,按照QPSO算法进行进化,从而解决了基于服务质量(Quality of Service,QoS)约束的Web服务组合问题,此为解决Web服务组合问题提出了一种新的思路.实验表明,使用QPSO...使用量子粒子群优化算法(QPSO),将可能的Web服务工作流执行路径看作粒子,按照QPSO算法进行进化,从而解决了基于服务质量(Quality of Service,QoS)约束的Web服务组合问题,此为解决Web服务组合问题提出了一种新的思路.实验表明,使用QPSO算法求解复杂Web服务组合问题在组合时间上具有一定的优越性.展开更多
基金the National Natural Science Foundation of China(No.61473144)。
文摘The computational speed in the feature selection of Mahalanobis-Taguchi system(MTS)using standard binary particle swarm optimization(BPSO)is slow and it is easy to fall into the locally optimal solution.This paper proposes an MTS variable optimization method based on chaos quantum-behavior particle swarm.In order to avoid the influence of complex collinearity on the distance measurement results,the Gram-Schmidt orthogonalization method is first used to calculate the Mahalanobis distance(MD)value.Then,the optimal threshold point of the system classification is determined through the receiver operating characteristic(ROC)curve;the misclassification rate and the selected variables are defined;the multi-objective mixed programming model is built.The chaos quantum-behavior particle swarm optimization(CQPSO)algorithm is proposed to solve the optimization combination,and the algorithm performs binary coding on the particle based on probability.Using the optimized combination of variables,a new Mahalanobis-Taguchi metric based prediction system is established to complete the task of precise discrimination.Finally,a fault diagnosis for the steel plate is taken as an example.The experimental results show that the proposed method can effectively enhance the iterative speed and optimization precision of the particles,and the prediction accuracy of the optimized MTS is significantly improved.
文摘使用量子粒子群优化算法(QPSO),将可能的Web服务工作流执行路径看作粒子,按照QPSO算法进行进化,从而解决了基于服务质量(Quality of Service,QoS)约束的Web服务组合问题,此为解决Web服务组合问题提出了一种新的思路.实验表明,使用QPSO算法求解复杂Web服务组合问题在组合时间上具有一定的优越性.
