An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems

Parameter estimation of chaotic systems is an important issue in the fields of computational mathematics and nonlinear science, which has gained increasing research and applications. In this paper, biogeography-based optimization (BBO), a new effective optimization algorithm based on the biogeography theory of the geographical distribution of biological organisms, is reasonably combined with differential evolution and simplex search to develop an effective hybrid algorithm for solving parameter estimation problem that is formulated as a multi-dimensional optimization problem. By suitably fusing several optimization methods with different searching mechanisms and features, the exploration and exploitation abilities of the hybrid algorithm can be enhanced and well balanced. Numerical simulation based on several typical chaotic systems and comparisons with some existing methods demonstrate the effectiveness of the proposed algorithm. In addition, the effects of population size and noise on the performances of the hybrid algorithm are investigated.