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Parameter Control Based Cuckoo Search Algorithm for Numerical Optimization

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Abstract

Cuckoo search (CS) algorithm is an efficient search technique for addressing numerical optimization problems. However, for the basic CS, the step size and mutation factor are sensitive to the optimization problems being solved. In view of this consideration, a new version namely the parameter control based CS (PCCS) algorithm is presented to strengthen the search accuracy and robustness. In this variant, the step size and mutation factor are dynamically updated according to the elite information stored in the historical archives at each generation, so as to realize the reasonable setting of these control parameters. For performance evaluation, numerical experiments are conducted on 25 benchmark functions from two different test suites. Moreover, the application in neural network optimization is also considered to further investigate the effectiveness. Experimental results indicate that the proposed PCCS algorithm is a promising and competitive method in terms of solution quality and convergence rate.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 51669006) and Scientific Research Foundation of Guilin University of Technology (No. RD2100002991).

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  1. The College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China

    Jiatang Cheng & Yan Xiong

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  1. Jiatang Cheng
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  2. Yan Xiong
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Correspondence to Yan Xiong.

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Cheng, J., Xiong, Y. Parameter Control Based Cuckoo Search Algorithm for Numerical Optimization. Neural Process Lett 54, 3173–3200 (2022). https://doi.org/10.1007/s11063-022-10758-0

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