基于文化差分进化算法的最小二乘支持向量机及QSAR建模  

Least Square Support Vector Machine Based on Cultural Differential Evolution Algorithm and Its Application to QSAR Modeling

作　　者：崔文同[1] 胡春平[1] 颜学峰[1] 

机构地区：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

出　　处：《华东理工大学学报（自然科学版）》2010年第1期121-125,共5页Journal of East China University of Science and Technology

基　　金：国家自然科学基金项目(20776042);国家863项目(2007AA04Z164);曙光计划项目(09SG29);博士点基金(20090074110005);上海市重点学科建设项目(B504)

摘　　要：针对最小二乘支持向量机最佳算法参数难以确定的缺陷,提出了基于文化差分进化算法的最小二乘支持向量机(Cultural Differential evolution Algorithm Least Square Support Vector Machine,CDE-LSSVM)。该算法通过新型的文化差分进化算法优化确定最小二乘支持向量机核宽度参数和惩罚系数,建立具有良好预测性能的模型。同时,针对药物定量构效关系(Quantitative Structure-Activity Relationships,QSAR)模型具有高度非线性、变量之间存在相关性的特征,采用CDE-LSSVM建立HIV-1蛋白酶抑制剂的药物定量构效关系模型。模型具有很好的拟合精度与预测精度,且优于最小二乘支持向量机、BP神经网络和径向基神经网络。In order to obtain the best parameters of least square support vector machine（LS-SVM）, a novel least square support vector machine algorithm integrating with cultural differential evolution （CDE- LSSVM） is proposed. In CDE-LSSVM, CDE algorithm is used to optimize the parameters of kernel width and the factor of punishment so as to obtain the model with better forecasting performance. Further, by considering that quantitative structure-activity relationships （QSAR） model is of high nonlinearity and has relativity between independent variables, CDE-LSSVM is applied to develop HIV-1 protease inhibitors QSAR model. In order to illustrate the performance of CDE-LSSVM model, LS-SVM, back-propagation neural networks and radial basis function neural network are employed respectively to develop the QSAR models. The simulation results show that CDE-LSSVM model is of better performance of fitting and forecasting.

关 键 词：文化差分进化算法 最小二乘支持向量机 药物定量构效关系 HIV-1蛋白酶抑制剂 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]