LS_SVM Parameters Selection Based on Hybrid Complex Particle Swarm Optimization

摘要

It is important to select parameters in the research area of support vector machine. For this reason, parameters selection for least squares support vector machine (LS_SVM) by hybrid complex particle swarm optimization is proposed in this paper. The proposed method reduces the disadvantage of traditional PSO in local optimum. Simulation of function estimation problem demonstrates t that LS_SVM based hybrid complex particle swarm optimization has better global optimization ability than LS_SVM based traditional PSO.