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A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation

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Abstract

This paper introduces a novel hybrid adaptive cuckoo search (HACS) algorithm to establish the parameters of chaotic systems. In order to balance and enhance the accuracy and convergence rate of the basic cuckoo search (CS) algorithm, the adaptive parameters adjusting operation is presented to tune the parameters properly. Besides, the exploitation capability of the CS algorithm is enhanced a lot by integrating the orthogonal design strategy. The functionality of the HACS algorithm is tested through the Lorenz system under the noise-free and noise-corrupted conditions, respectively. The numerical results demonstrate that the algorithm can estimate parameters efficiently and accurately, and the capability of noise immunity is also powerful. Compared with the basic CS algorithm, genetic algorithm, and particle swarm optimization algorithm, the HACS algorithm is energy efficient and superior.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant No. 61271106.

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Authors and Affiliations

  1. National Key Laboratory on Electromagnetic Environmental Effects and Electro-optical Engineering, PLA University of Science and Technology, Nanjing, 210007, Jiangsu, People’s Republic of China

    Jun Wang & Bihua Zhou

Authors
  1. Jun Wang
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  2. Bihua Zhou
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Correspondence to Jun Wang.

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Wang, J., Zhou, B. A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation. Neural Comput & Applic 27, 1511–1517 (2016). https://doi.org/10.1007/s00521-015-1949-1

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  • DOI: https://doi.org/10.1007/s00521-015-1949-1

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