采用云量子PSO的属性约简方法
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摘要
针对粒子群优化算法在处理信息系统中属性约简收敛速度慢、早熟的问题,提出了一种结合云模型的量子粒子群优化算法(CQPSO)的属性约简方法。改进量子粒子群优化算法,即利用量子粒子群算法的量子行为来加快收敛速度;引入云模型控制粒子种群在不同状态下进行寻优;根据属性依赖度等性质构造属性约简数学模型;采用CQPSO算法对其进行求解,得到约简结果。实验中采用标准测试函数对CQPSO算法进行仿真对比,验证了CQPSO算法性能优于量子PSO算法;采用UCI标准数据库的典型例子进行属性约简测试,结果表明提出的属性约简方法优于现有约简方法,其计算速度快、识别精度高。
In the processing information system, the particle swarm optimization algorithm is applied for the minimum attribute reduction, which is slow and easy to fall into local optimum. Accordingly, this paper proposes a quantum-behaved particle swarm optimization algorithm combined with cloud model(CQPSO)to reduce the number of attributes in data set. First, the speed of convergence is accelerated by using a quantum behavior of QPSO algorithm; and the cloud model is introduced into QPSO to control different particle swarms in different states; then, the attribute reduction mathematical model is constructed according to property dependency and other properties; finally, the CQPSO algorithm is used to solve the problem and achieve the reduction results. In this experiment, the CQPSO algorithm is simulated and compared by the standard test function, which shows that the CQPSO algorithm performance is better than the quantum-behaved PSO algorithm. And the UCI standard database is used to perform attribute reduction tests. The results show that the proposed attribute reduction method is superior to the existing reduction method, and its calculation speed is fast and the recognition precision is high.
In the processing information system, the particle swarm optimization algorithm is applied for the minimum attribute reduction, which is slow and easy to fall into local optimum. Accordingly, this paper proposes a quantum-behaved particle swarm optimization algorithm combined with cloud model(CQPSO)to reduce the number of attributes in data set. First, the speed of convergence is accelerated by using a quantum behavior of QPSO algorithm; and the cloud model is introduced into QPSO to control different particle swarms in different states; then, the attribute reduction mathematical model is constructed according to property dependency and other properties; finally, the CQPSO algorithm is used to solve the problem and achieve the reduction results. In this experiment, the CQPSO algorithm is simulated and compared by the standard test function, which shows that the CQPSO algorithm performance is better than the quantum-behaved PSO algorithm. And the UCI standard database is used to perform attribute reduction tests. The results show that the proposed attribute reduction method is superior to the existing reduction method, and its calculation speed is fast and the recognition precision is high.
引文
[1]Jia X,Shang L,Zhou B,et al.Generalized attribute reduct in rough set theory[J].Knowledge-Based Systems,2016,91(1):204-218.
[2]Song J,Tsang E C C,Chen D,et al.Minimal decision cost reduct in fuzzy decision-theoretic rough set model[J].Knowledge-Based Systems,2017,13(3):1-9.
[3]Hu Q,Zhang L,Zhou Y,et al.Large-scale multi-modality attribute reduction with multi-kernel fuzzy rough sets[J].IEEE Transactions on Fuzzy Systems,2017.
[4]Dai S P,Liu S J,Zheng S F.A GA-PSO based attribute reduction algorithm for rough set[J].Computer Engineering&Science,2015,37(2):397-401.
[5]Konar P,Sil J,Chattopadhyay P.Knowledge extraction using data mining for multi-class fault diagnosis of induction motor[J].Neurocomputing,2015,166:14-25.
[6]Inbarani H H,Azar A T,Jothi G.Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis[J].Computer Methods&Programs in Biomedicine,2014,113(1).
[7]周恺,王艳,纪志成.混合量子粒子群算法求解模具车间调度问题[J].系统仿真学报,2016,28(6):1247-1254.
[8]Yao B,Yu B,Hu P,et al.An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot[J].Annals of Operations Research,2016,242(2):1-18.
[9]Sun J,Wu X,Palade V,et al.Convergence analysis and improvements of quantum-behaved particle swarm optimization[J].Information Sciences,2012,193(15):81-103.
[10]Liu L.Routing of logistics distribution vehicles using cloud adaptive mean particle swarm optimization[J].International Journal of Simulation—Systems,Science&Techno,2016,15(17).
[11]Berndt D J,Watkins A.Investigating the performance of genetic algorithm-based software test case generation[C]//High Assurance Systems Engineering,2004.
[12]丁进良,杨翠娥,陈立鹏,等.基于参考点预测的动态多目标优化算法[J].自动化学报,2017,43(2):313-320.
[13]Pawlak Z.Theoretical aspect of reasoning about data[M]//Rough sets:theoretical aspects of reasoning about data.[S.l.]:Kluwer Academic Publishers,1991.
[14]Antón J Cá,Nieto P J G,Gonzalo E G,et al.A new predictive model for the state-of-charge of a high-power lithium-ion cell based on a PSO-optimized multivariate adaptive regression spline approach[J].IEEE Transactions on Vehicular Technology,2016,65(6):4197-4208.
[15]Guo X,Peng C,Zhang S,et al.A novel feature extraction approach using window function capturing and QPSOSVM for enhancing electronic nose performance[J].Sensors,2015,15(7):15198-15217.
[2]Song J,Tsang E C C,Chen D,et al.Minimal decision cost reduct in fuzzy decision-theoretic rough set model[J].Knowledge-Based Systems,2017,13(3):1-9.
[3]Hu Q,Zhang L,Zhou Y,et al.Large-scale multi-modality attribute reduction with multi-kernel fuzzy rough sets[J].IEEE Transactions on Fuzzy Systems,2017.
[4]Dai S P,Liu S J,Zheng S F.A GA-PSO based attribute reduction algorithm for rough set[J].Computer Engineering&Science,2015,37(2):397-401.
[5]Konar P,Sil J,Chattopadhyay P.Knowledge extraction using data mining for multi-class fault diagnosis of induction motor[J].Neurocomputing,2015,166:14-25.
[6]Inbarani H H,Azar A T,Jothi G.Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis[J].Computer Methods&Programs in Biomedicine,2014,113(1).
[7]周恺,王艳,纪志成.混合量子粒子群算法求解模具车间调度问题[J].系统仿真学报,2016,28(6):1247-1254.
[8]Yao B,Yu B,Hu P,et al.An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot[J].Annals of Operations Research,2016,242(2):1-18.
[9]Sun J,Wu X,Palade V,et al.Convergence analysis and improvements of quantum-behaved particle swarm optimization[J].Information Sciences,2012,193(15):81-103.
[10]Liu L.Routing of logistics distribution vehicles using cloud adaptive mean particle swarm optimization[J].International Journal of Simulation—Systems,Science&Techno,2016,15(17).
[11]Berndt D J,Watkins A.Investigating the performance of genetic algorithm-based software test case generation[C]//High Assurance Systems Engineering,2004.
[12]丁进良,杨翠娥,陈立鹏,等.基于参考点预测的动态多目标优化算法[J].自动化学报,2017,43(2):313-320.
[13]Pawlak Z.Theoretical aspect of reasoning about data[M]//Rough sets:theoretical aspects of reasoning about data.[S.l.]:Kluwer Academic Publishers,1991.
[14]Antón J Cá,Nieto P J G,Gonzalo E G,et al.A new predictive model for the state-of-charge of a high-power lithium-ion cell based on a PSO-optimized multivariate adaptive regression spline approach[J].IEEE Transactions on Vehicular Technology,2016,65(6):4197-4208.
[15]Guo X,Peng C,Zhang S,et al.A novel feature extraction approach using window function capturing and QPSOSVM for enhancing electronic nose performance[J].Sensors,2015,15(7):15198-15217.