基于改进量子粒子群算法的支持向量机参数优化方法  

An Optimization Method of SVM Parameters Based on Improved QPSO Algorithm

作　　者：周頔[1,2] ZHOU Di(Dazhou Industrial Technological Institute of Intelligent Manufacturing,Sichuan University of Arts and Science,Dazhou 635000,China;Artificial Intelligence and Health Monitoring Laboratory,Chongqing University,Chongqing 400044,China)

机构地区：[1]四川文理学院达州智能制造产业技术研究院,四川达州635000 [2]重庆大学人工智能与健康监护实验室,重庆400044

出　　处：《计算机与现代化》2018年第9期27-31,共5页Computer and Modernization

基　　金：四川省科技厅应用基础重点项目(2018JY0129);四川省教育厅科技计划项目(18ZA0415)

摘　　要：支持向量机参数的选择对建模精度和泛化性能等有着重要的影响,提出量子粒子群优化(QPSO)改进算法优化支持向量机(SVM)参数的方法。该方法首先将混合扰动算子引入QPSO算法中,用于获取平均最优位置,建立一种基于混合扰动算子的QPSO算法改进方法(IQPSO),然后用IQPSO算法的全局优化能力对支持向量机惩罚系数和核参数进行综合寻优,求取最优化参数组合,从而提高支持向量机的求解速度和解的精确性。利用测试函数和UCI测试数据,对IQPSO-SVM进行仿真测试与分类,实验结果表明,IQPSO能获得很好的优化结果,IQPSO-SVM具有较好的泛化性能。Because the parameter selection of support vector machine（ SVM） has an important influence on the modeling precision and generalization performance,an optimization method of SVM parameters based on improved QPSO algorithm（ IQPSOSVM） is proposed. In the IQPSO-SVM method,the mixed disturbance operator is introduced into QPSO algorithm in order to obtain the average optimization position to construct an improved QPSO（ IQPSO） algorithm. Then the IQPSO algorithm with the global optimization ability is used to optimize the penalty coefficient and kernel parameter of SVM model in order to obtain the optimal combination values of parameters and improve the accuracy and solving speed for SVM model. The test functions and UCI data are used to test and verify the effectiveness of the proposed IQPSO-SVM method. The experimental results show that IQPSO can obtain better optimization effect,and IQPSO-SVM has good generalization performance.

关 键 词：改进量子粒子群 支持向量机 参数优化 混合扰动算子 性能 

分 类 号：TH164[机械工程—机械制造及自动化]