Optimizing parameters of LS-SVM based on chaotic ant swarm algorithm

Appropriate parameters are very crucial to the learning performance and generalization ability of least-squares support vector machines (LS-SVM). In this paper, a novel parameter selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. The selection problem of LS-SVM parameters is considered as a compound optimization problem. Then objective function of optimization problem is set and a CAS optimization algorithm is employed to search optimal objective function. CAS algorithm is global search method and it need not to consider LS-SVM dimensionality and complexity. The simulation results show that the proposed method is an effective approach for parameter optimization and the good performance for function approximation is obtained.