An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems |
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| Authors: | Ling Wang Ye Xu |
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| Affiliation: | 1. Road Management Division, Seoul Metropolitan Government, South Korea;2. Department of Civil Engineering, Seoul National University of Science & Technology, South Korea;1. College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China;2. Henan Key Lab of Information-based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou 450002, China;3. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran;2. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran;1. State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan 430072, China;2. Research Institute of Numerical Computation and Engineering Applications, North University for Nationalities, Yinchuan 750021, China |
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| Abstract: | 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. |
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