基于粒子群优化算法的支持向量机研究

基于粒子群优化算法提出了一种通过优化支持向量机模型参数，建立更佳的支持向量机数学模型的方法. 针对双螺旋分类问题，分别利用基于粒子群优化算法所建立的支持向量机分类器和标准支持向量机分类器进行了仿真实验，利用所建立的评价体系对仿真实验所获得的实验数据进行了评估，评估结果表明基于粒子群优化算法的支持向量机分类器明显优于标准支持向量机分类器，其分类结果表明基于粒子群优化算法的支持向量机分类器提高了分类结果的准确性，同时也验证了基于粒子群优化算法的支持向量机分类器在数据分类中的有效性. 

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.