An adaptive chaotic PSO for parameter optimization and feature extraction of LS-SVM based modelling

While training an LS-SVM model, two main challenges are parameter optimization and input feature extraction. The main purpose of this article is to address these two problems. Commonly used tools are PSO and BPSO, but they are not suitable for the optimization issues of many local optima owing to its random numbers used to update velocities. In this paper, an adaptive chaotic particle swarm optimization (cPSO) algorithm is proposed to enhance its global searching capability and local searching capability. The practicality of the proposed scheme is demonstrated by application to mineral process for the prediction models between production rate of the concentrated ore and the technical indexes. Compared with the original methods of grid search+PCA, GA+PCA, PSO+PCA as well as PSO+BPSO, the proposed strategy outperforms these existing methods in terms of convergence accuracy.