Method of Parameters Optimization in SVM based on PSO

To overcome the uncertainty and to resolve the problem of parameters optimization in kernel function of support vector machine (SVM), particle swarm optimization (PSO) method, which was originated form artificial life and evolutionary computation, is applied to SVM’s parameters selection and optimization in the paper. The improved PSO algorithm of increasing convergence rate is proposed based on the analyzingprinciple of basic PSO. Thereupon, the improved PSO algorithm has self- adaptive ability that can be faster searching in early phase and more carefully searching in latter phase rather than basic PSO, and can be meeting the requests of diversification and intensification. The simulation experiment results demonstrate that, the selected kernel parameters by the new PSO algorithm can improve the overall performance of the SVM classifier and have new application domain.