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Neural Processing Letters
Erschienen in: Neural Processing Letters 4/2022

22.02.2022

Parameter Control Based Cuckoo Search Algorithm for Numerical Optimization

verfasst von: Jiatang Cheng, Yan Xiong

Erschienen in: Neural Processing Letters | Ausgabe 4/2022

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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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Metadaten
Titel
Parameter Control Based Cuckoo Search Algorithm for Numerical Optimization
verfasst von
Jiatang Cheng
Yan Xiong
Publikationsdatum
22.02.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 4/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10758-0

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