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.