Research on Support Vector Machine Based on Particle Swarm Optiminzation

A method was presented to establish a support vector machine model based on particle swarm optimization algorithm. The model is a better mathematical model established by optimizing the parameters of support vector machine. Particle swarm optimization algorithm is a bionic intelligent algorithm, showing a strong capability in global search. In this paper, particle swarm optimization algorithm was taken to optimize the SVM mathematical model parameters, so as to achieve optimal support vector machine model. In order to solve the double helix classification problem, the particle swarm optimization algorithm based on the established support vector machine classifier and the traditional support vector machine classifier were simulated respectively. The simulation results were evaluated with an evaluation system established for the simulation. The evaluation results showed that the particle swarm optimization algorithm based on support vector machine classifier is better than that based on traditional SVM classifier. The classification results showed that the particle swarm optimization algorithm based on support vector machine classifier can improve the classification accuracy, and also validated its validity in the data classification.