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Cuckoo search algorithm with memory and the vibrant fault diagnosis for hydroelectric generating unit

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Abstract

Levy flight random walk is one of the important operators of cuckoo search (CS) algorithm, and it employs the fixed step size factor to generate new candidate solutions. In this work, the memory mechanism is introduced into CS algorithm to dynamically select the appropriate step size, which differs from many CS variants by incorporating some existing algorithms into CS framework. To investigate the effectiveness of the presented version, two well-known test suites are employed. Experimental results demonstrate that CS with memory (CSM) exhibits better optimization performance compared with other CS variants. Then, a vibration fault diagnosis model of hydroelectric generating unit (HGU) based on CSM combined with BP neural network is established. Diagnostic results show that the combined model has higher classification accuracy in tackling two diagnostic examples, and also prove the superiority of the proposed algorithm in solving practical problems.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (51669006, 61773314).

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Authors and Affiliations

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

    Jiatang Cheng & Lei Wang

  2. The Engineering College, Honghe University, Mengzi, 661199, China

    Jiatang Cheng & Yan Xiong

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  1. Jiatang Cheng
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  2. Lei Wang
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  3. Yan Xiong
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Correspondence to Lei Wang.

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Cheng, J., Wang, L. & Xiong, Y. Cuckoo search algorithm with memory and the vibrant fault diagnosis for hydroelectric generating unit. Engineering with Computers 35, 687–702 (2019). https://doi.org/10.1007/s00366-018-0627-1

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