基于佳点集粒子群算法的SVM参数优化方法

针对传统的粒子群算法（PSO）初始种群随机生成而导致的算法稳定性差和易出现早熟等问题，提出了基于佳点集改进的粒子群算法（GSPSO），并将其优化支持向量机（SVM），构建一种高效的预测评估模型（GSPSO-SVM）。首先采用佳点集方法使PSO中初始粒子均匀分布，然后利用GSPSO优化SVM的惩罚因子C和径向基核函数参数g以获取最佳参数值，提高SVM分类性和稳定性，最后将模型应用于旱情数据的评估预测。仿真实验结果表明：本模型在平均准确率和方差方面的准确都取得了很好的效果；对比分别用PSO和遗传算法（GA）优化的SVM模型，本模型的性能更好。

Parameter Optimization Method of SVM Based on Good-Point Set Particle Swarm Optimization

In order to solve the problems of poor stability and premature phenomenon caused by the initially randomly generated population in traditional particle swarm optimization (PSO) algorithm, a good-point set particle swarm optimization (GSPSO) was proposed. Based on that, an efficient prediction model (GSPSO-SVM) was constructed by combining support vector machine (SVM). The good-point set was utilized to make the initial particles uniformly distributed in PSO algorithm, and then GSPSO was used to optimize the penalty factor C and radial basis function parameter g of SVM to obtain the best parameter for improving the accuracy and stability of classification of SVM. Finally, the model was successfully applied to the drought forecasting. The simulation results showed that the model have achieved good results in average accuracy and variance. Compared with PSO and genetic algorithm (GA) to optimize SVM model, the performance has been improved.