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An improved firefly algorithm for identifying parameters of nonlinear empirical models
Joint Authors
Yan, Jingwen
Xiao, Chi
Tian, Xiaoxia
Source
Journal of Engineering Research
Issue
Vol. 9, Issue 4 A (31 Dec. 2021), pp.74-86, 13 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2021-12-31
Country of Publication
Kuwait
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
The nonlinear model is to describe the vortex-induced resonance of long-span bridges under the action of natural wind.
The identification accuracy of its parameters directly affects people's understanding of vortex-induced vibration.
People have been trying different algorithms to solve this parameter identification problem, but the efficiency and accuracy of algorithms are not satisfied.
In this work, a firefly algorithm based on local chaos search and brightness variant (FACLBV) is proposed.
The characteristics of chaos make FACLBV search the widely local scope and improve the accuracy of the solution.
FACLBV modifies the fixed initial brightness, discards the absorption coefficient of light intensity, links the initial brightness of every firefly with the position of its solution space, and sets the attraction of every firefly as a simple linear function, which reduces the complexity of the algorithm and improves the efficiency.
In order to better verify the superiority of FACLBV, the simulation experiment includes three parts: the comparison between FACLBV and other firefly algorithms, the verification of the parameters identified by FACLBV, and the nonparametric test between FACLBV and other intelligent algorithms.
Simulation results show that FACLBV is better than other algorithms in performance.
American Psychological Association (APA)
Tian, Xiaoxia& Xiao, Chi& Yan, Jingwen. 2021. An improved firefly algorithm for identifying parameters of nonlinear empirical models. Journal of Engineering Research،Vol. 9, no. 4 A, pp.74-86.
https://search.emarefa.net/detail/BIM-1494704
Modern Language Association (MLA)
Tian, Xiaoxia…[et al.]. An improved firefly algorithm for identifying parameters of nonlinear empirical models. Journal of Engineering Research Vol. 9, no. 4 A (Dec. 2021), pp.74-86.
https://search.emarefa.net/detail/BIM-1494704
American Medical Association (AMA)
Tian, Xiaoxia& Xiao, Chi& Yan, Jingwen. An improved firefly algorithm for identifying parameters of nonlinear empirical models. Journal of Engineering Research. 2021. Vol. 9, no. 4 A, pp.74-86.
https://search.emarefa.net/detail/BIM-1494704
Data Type
Journal Articles
Language
English
Record ID
BIM-1494704