A Cost Forecasting Approach Based on Support Vector Machine with Adaptive Particle Swarm Optimization Algorithm

摘要：

A novel adaptive particle swarm optimization (APSO) algorithm based on population diversity information is presented to solve the precocious convergence problem of particle swarm optimization algorithm. The APSO algorithm uses the information of the population diversity to adjust nonlinearly inertia weight. Velocity mutation factor and position interchange factor are both introduced and the global performance is clearly improved.The APSO algorithm is applied to optimization of parameters in the cost forecasting model based on support vector machine (SVM) and a cast forecasting model based on SVM with APSO algorithm (APSO-SVM) is established.The simulation result shows that the prediction accuracy of APSO-SVM is higher than other traditional methods of cost forecasting, so using APSO-SVM method to forecast cost is feasible and effective.

关键词： Particle Swarm Optimization (PSO) adaptive variance support vector machine cost forecasting

作者: Jing Han Xi Chen Feng Kang

作者单位: School of Management Northeastern University Shenyang City, P. R. China

会议类型: 国际会议

会议名称: 2007 Conference on Systems Science, Management Science and System Dynamics(第二届系统科学、管理科学与系统动力学国际会议)

会议地点: 上海

会议语种:英文

页码: 601-608

在线出版日期: 2007-10-19（万方平台首次上网日期，不代表论文的发表时间）