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Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem

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Abstract

The design method of adaptive infinite impulse response (IIR) filter is a challenging problem. Its design principle is to determine the filter parameters by the iteration process of the adaptive algorithm, which is to obtain an optimal model for unknown plant based on minimizing mean square error (MSE). However, many adaptive algorithms cannot adjust the parameters of IIR filter to the minimum MSE. Therefore, a more efficient adaptive optimization algorithm is required to adjust the parameters of IIR filter. In this paper, we propose a selfish herd optimization algorithm based on chaotic strategy (CSHO) and apply it to solving IIR system identification problem. In CSHO, we add a chaotic search strategy, which is a better local optimization strategy. Its function is to search for better candidate solutions around the global optimal solution, which makes the local search of the algorithm more precise and finds out potential global optimal solutions. We use solving IIR system identification problem to verify the effectiveness of CSHO. Ten typical IIR filter models with the same order and reduced order are selected for experiments. The experimental results of CSHO compare with those of bat algorithm (BA), cellular particle swarm optimization and differential evolution (CPSO-DE), firefly algorithm (FFA), hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), improved particle swarm optimization (IPSO) and opposition-based harmony search algorithm (OHS), respectively. The experimental results show that CSHO has better optimization accuracy, convergence speed and stability in solving most of the IIR system identification problems. At the same time, it also obtains better optimization parameters and achieves smaller difference between actual output and expected output in test samples.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This paper has been awarded by the National Natural Science Foundation of China (61170035, 61272420, 81674099 and 61502233), the Fundamental Research Fund for the Central Universities (30916011328, 30918015103 and 30918012204), Nanjing Science and Technology Development Plan Project (201805036) and “13th Five-Year” Equipment Field Fund (61403120501).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Ruxin Zhao, Yongli Wang, Peng Hu, Hamed Jelodar, Chi Yuan, YanChao Li, Isma Masood, Mahdi Rabbani, Hao Li & Bo Li

  2. CETC Big Data Research Institute Co., Ltd., Guiyang, 550081, Guizhou, China

    Yongli Wang

  3. School of Mechanical and Automotive Engineering, Yangzhou Polytechnic Institute, Yangzhou, 225127, Jiangsu, China

    Chang Liu

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  1. Ruxin Zhao
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  2. Yongli Wang
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  3. Chang Liu
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  4. Peng Hu
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Correspondence to Yongli Wang.

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Zhao, R., Wang, Y., Liu, C. et al. Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem. Soft Comput 24, 7637–7684 (2020). https://doi.org/10.1007/s00500-019-04390-9

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