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A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction

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Abstract

Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching–learning-based optimization (TLBO)–differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO–DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.

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Acknowledgments

This research was partially supported by National Natural Science Foundation of China (61100173, 61272283 and 61304082). This work is partially supported by the Natural Science Foundation of Anhui Province, China (Grants No. 1308085MF82). This work is also supported by Doctoral Innovation Foundation of Xi’an University of Technology (207-002J1305). We would like to acknowledge http://www.york.ac.uk/depts/maths/data/ts/ for providing us the data sets. The authors would like to thank the editor and reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Lei Wang, Feng Zou, Xinhong Hei, Dongdong Yang, Qiaoyong Jiang & Zijian Cao

  2. School of Physics and Electronic Information, Huaibei Normal University, Huaibei, 235000, China

    Feng Zou & Debao Chen

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  1. Lei Wang
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  2. Feng Zou
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  3. Xinhong Hei
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  4. Dongdong Yang
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  7. Zijian Cao
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Correspondence to Feng Zou.

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Wang, L., Zou, F., Hei, X. et al. A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction. Neural Comput & Applic 25, 1407–1422 (2014). https://doi.org/10.1007/s00521-014-1627-8

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  • DOI: https://doi.org/10.1007/s00521-014-1627-8

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