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Fractional chaos maps with flower pollination algorithm for chaotic systems� parameters identification

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dc.citedby9
dc.contributor.authorYousri D.en_US
dc.contributor.authorAllam D.en_US
dc.contributor.authorBabu T.S.en_US
dc.contributor.authorAbdelAty A.M.en_US
dc.contributor.authorRadwan A.G.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorEteiba M.B.en_US
dc.contributor.authorid56688582500en_US
dc.contributor.authorid55940454800en_US
dc.contributor.authorid56267551500en_US
dc.contributor.authorid57191328816en_US
dc.contributor.authorid7103379659en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid6603527538en_US
dc.date.accessioned2023-05-29T08:07:26Z
dc.date.available2023-05-29T08:07:26Z
dc.date.issued2020
dc.descriptionChaotic systems; DC motors; Heuristic algorithms; Optimization; Benchmark functions; Chaotic behaviors; Convergence speed; Evaluation function; Meta-heuristic optimizations; Non-parametric statistical tests; Optimization problems; Parameters identification; Parameter estimationen_US
dc.description.abstractMeta-heuristic optimization algorithms are the new gate in solving most of the complicated nonlinear systems. So, improving their robustness, reliability, and convergence speed is the main target to meet the requirements of various optimization problems. In the current work, three different fractional-order chaos maps (FC-maps), which have been introduced recently, are incorporated with the fundamental flower pollination algorithm to tune its parameters adaptively. These maps are fractional logistic map, fractional sine map, fractional tent map, and their integer-order versions. As a result, fractional chaotic FPA (FC-FPA) is proposed. The FC-FPA has been mathematically tested over 10-, 30-, 50-, and 100-dimensional CEC 2017 benchmark functions. Moreover, the influence of merging FC-maps with FPA is investigated in case of increasing the number of maximum evaluation functions based on the ten functions of CEC 2020. Additionally, to assess the superiority of the proposed FC-FPA algorithm for more complicated optimization problems, it has been tested to extract the parameters of different chaotic systems with and without added noise. In addition, it is tested on the identification of the corresponding parameters for the chaotic behavior in brush-less DC motor. The results of the fractional version of CFPA are compared with that of integer CFPA and standard FPA via an extensive statistical analysis. Furthermore, a nonparametric statistical test is employed to affirm the superiority of the proposed fractional variants of CFPA. It is evident that the performance of FPA is highly influenced by integrating the fractional-order chaos maps as the introduced FC-FPA variants provide a better accurate and more consistent results as well as a higher speed of convergence especially upon using the fractional sine map. � 2020, Springer-Verlag London Ltd., part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00521-020-04906-7
dc.identifier.epage16327
dc.identifier.issue20
dc.identifier.scopus2-s2.0-85084288542
dc.identifier.spage16291
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084288542&doi=10.1007%2fs00521-020-04906-7&partnerID=40&md5=80b46535ecc4a9cf40b6cfa4a78acc67
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25224
dc.identifier.volume32
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleNeural Computing and Applications
dc.titleFractional chaos maps with flower pollination algorithm for chaotic systems� parameters identificationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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