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Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm

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Abstract

Biogeography-based optimization algorithm (BBO) is a relatively new optimization technique which has been shown to be competitive to other biology-based algorithms. However, there is still an insufficiency in BBO regarding its migration operator, which is good at exploitation but poor at exploration. To address this concerning issue, we propose an improved BBO (IBBO) by using a modified search strategy to generate a new mutation operator so that the exploration and exploitation can be well balanced and then satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, both opposition-based learning methods and chaotic maps are employed, when producing the initial population. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as high-dimension numerical optimization problems with multiple local optima. Numerical simulations and comparisons with some typical existing algorithms demonstrate the effectiveness and efficiency of the proposed approach.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (Grant No. 60974082), the Fundamental Research Funds for the Central Universities (Grant No. JY10000970006), the Science Plan Foundation of the Education Bureau of Shaanxi Province (Grant Nos. 09JK722 and 11JK1051) and Foundation of the Education Bureau of Guangxi Region (No. 200812LX85).

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Authors and Affiliations

  1. Department of Mathematics, Xidian University, Xi’an, Shannxi, 710071, P.R. China

    Guo-Ping Yang & San-Yang Liu

  2. School of Science, Xi’an University of Posts and Telecommunications, Xi’an, 710121, P.R. China

    Jian-Ke Zhang

  3. College of Science, Guilin University of Technology, Guilin, Guangxi, 541004, P.R. China

    Quan-Xi Feng

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  1. Guo-Ping Yang
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  2. San-Yang Liu
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  3. Jian-Ke Zhang
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  4. Quan-Xi Feng
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Correspondence to Guo-Ping Yang.

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Yang, GP., Liu, SY., Zhang, JK. et al. Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm. Appl Intell 39, 132–143 (2013). https://doi.org/10.1007/s10489-012-0398-0

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  • DOI: https://doi.org/10.1007/s10489-012-0398-0

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