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

Abstract: 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 (IQPSO-SVM) 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.

Key words:  improved quantum particle swarm optimization, support vector machine, parameter optimization, mixed disturbance operator, performance