Combination of particle-swarm optimization with least-squares support vector machine for FDTD time series forecasting

Abstract

A new combination of particle-swarm optimization (PSO) and the least-squares support vector machine (LS-SVM) technique for FDTD time-series forecasting is presented. In this paper, the PSO is extended to optimize the hyperparameter used in the LS-SVM algorithm. The numerical simulations demonstrate that the PSO method can efficiently obtain the optimal value of the hyperparameter used in the LS-SVM algorithm. And the PSO_LS-SVM method can improve the computational efficiency of the FDTD algorithm, as compared to the direct FDTD method. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 141–144, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21288