Grid Resources Prediction with Support Vector Regression and Particle Swarm Optimization

摘要：

Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resource prediction. In order to obtain better prediction performance, SVRs parameters must be selected carefully. Therefore, a particle swarm optimization-based SVR (PSO-SVR) model, in which PSO is used to determine free parameters of SVR, is presented in this study. The hybrid model (PSO-SVR) can automatically determine the parameters of SVR with higher predictive accuracy and generalization ability simultaneously. The performance of PSO-SVR, the back-propagation neural network (BPNN) and the traditional SVR model whose parameters are obtained by trail-and-error procedure (T-SVR) have been compared with benchmark data set. Experimental results indicate that the PSO-SVR model can achieve higher predictive accuracy than the other two models.

关键词： grid resources prediction support vector regression particle swarm optimization

作者: Guosheng Hu Liang Hu Hongwei Li Kun Li Wei Liu

作者单位: College of Computer Science and Technology, Jilin University, ChangChun, 130012, China

会议类型: 国际会议

会议名称: The Third International Joint Conference on Computational Science and Optimization(第三届计算科学与优化国际大会 CSO 2010)

会议地点: 黄山

会议语种:英文

页码: 417-422

在线出版日期: 2010-05-28（万方平台首次上网日期，不代表论文的发表时间）