Fractional-order cuckoo search algorithm for parameter identification of the fractional-order chaotic, chaotic with noise and hyper-chaotic financial systems
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Author(s)
Yousri, Dalia
Mirjalili, Seyedali
Griffith University Author(s)
Year published
2020
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Identifying the parameters of the chaos phenomena in the economic-financial systems is a critical issue to control and avoid the financial crises and bogging the market down. Therefore, in this paper, an efficient and reliable optimization algorithm is developed to identify the corresponding parameters of that chaotic dynamical behavior in the fractional-order chaotic, chaotic with noise, and hyper-chaotic financial systems. The introduced algorithm is a cooperation among the fractional calculus (FC) perspective and the basic cuckoo search algorithm to enhance the stochastic cuckoo’s walk via considering the cuckoo’s earlier ...
View more >Identifying the parameters of the chaos phenomena in the economic-financial systems is a critical issue to control and avoid the financial crises and bogging the market down. Therefore, in this paper, an efficient and reliable optimization algorithm is developed to identify the corresponding parameters of that chaotic dynamical behavior in the fractional-order chaotic, chaotic with noise, and hyper-chaotic financial systems. The introduced algorithm is a cooperation among the fractional calculus (FC) perspective and the basic cuckoo search algorithm to enhance the stochastic cuckoo’s walk via considering the cuckoo’s earlier behaviors from memory. The developed fractional-order cuckoo search (FO-CS) is validated with twenty-eight functions of CEC2017 with different dimensions. Several measures and non-parametric statistical tests are presented to demonstrate the superiority of the introduced algorithm while compared with the CS and the state-of-the-art techniques. The results show that merging of FC properties magnifies CS’s efficiency, convergence speed, and robustness against the complexity of the considered CEC benchmarks suite and the non-linearity of the fractional-order chaotic, chaotic with noise, and hyper-chaotic financial systems.
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View more >Identifying the parameters of the chaos phenomena in the economic-financial systems is a critical issue to control and avoid the financial crises and bogging the market down. Therefore, in this paper, an efficient and reliable optimization algorithm is developed to identify the corresponding parameters of that chaotic dynamical behavior in the fractional-order chaotic, chaotic with noise, and hyper-chaotic financial systems. The introduced algorithm is a cooperation among the fractional calculus (FC) perspective and the basic cuckoo search algorithm to enhance the stochastic cuckoo’s walk via considering the cuckoo’s earlier behaviors from memory. The developed fractional-order cuckoo search (FO-CS) is validated with twenty-eight functions of CEC2017 with different dimensions. Several measures and non-parametric statistical tests are presented to demonstrate the superiority of the introduced algorithm while compared with the CS and the state-of-the-art techniques. The results show that merging of FC properties magnifies CS’s efficiency, convergence speed, and robustness against the complexity of the considered CEC benchmarks suite and the non-linearity of the fractional-order chaotic, chaotic with noise, and hyper-chaotic financial systems.
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Journal Title
Engineering Applications of Artificial Intelligence
Volume
92
Copyright Statement
© 2020 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
Subject
Engineering
Information and computing sciences
Science & Technology
Automation & Control Systems
Computer Science, Artificial Intelligence
Engineering, Multidisciplinary