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Multi-objective metaheuristics applied for the multivariable system identification.

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Title: Multi-objective metaheuristics applied for the multivariable system identification.
Authors: Ferrari, A. C. K.1 (AUTHOR) allan.ferrari@ufpr.br, Leandro, G. V.1 (AUTHOR), Coelho, L. dos S.1 (AUTHOR)
Source: International Journal of Parallel, Emergent & Distributed Systems. Aug2025, Vol. 40 Issue 4, p424-445. 22p.
Subjects: System identification, MIMO systems, Grey Wolf Optimizer algorithm, Mathematical optimization, Genetic algorithms, Multi-objective optimization
Abstract: The mathematical modeling of a multivariable system is a challenging engineering problem for classical mathematical optimization schemes. This study implements and compares multi-objective metaheuristics—Multi-objective Grey Wolf Optimizer (MOGWO), Nondominated Sorting Genetic Algorithm II (NSGA-II), and Multi-objective Cuckoo Search (MOCS)—for the identification of MIMO (Multiple-Input Multiple-Output) systems. Through 100 independent runs, these multi-objective algorithms were evaluated against their single-objective counterparts using performance metrics such as the Coefficient of Determination ( $ R^2 $ R 2 ) and Mean Squared Error (MSE). The results demonstrate that multi-objective metaheuristics outperform their single-objective variants, proving an effective alternative for accurate and efficient MIMO system identification. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 187023040
AccessLevel: 6
PubType: Academic Journal
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  Data: Multi-objective metaheuristics applied for the multivariable system identification.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Parallel%2C+Emergent+%26+Distributed+Systems%22">International Journal of Parallel, Emergent & Distributed Systems</searchLink>. Aug2025, Vol. 40 Issue 4, p424-445. 22p.
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  Data: <searchLink fieldCode="DE" term="%22System+identification%22">System identification</searchLink><br /><searchLink fieldCode="DE" term="%22MIMO+systems%22">MIMO systems</searchLink><br /><searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The mathematical modeling of a multivariable system is a challenging engineering problem for classical mathematical optimization schemes. This study implements and compares multi-objective metaheuristics—Multi-objective Grey Wolf Optimizer (MOGWO), Nondominated Sorting Genetic Algorithm II (NSGA-II), and Multi-objective Cuckoo Search (MOCS)—for the identification of MIMO (Multiple-Input Multiple-Output) systems. Through 100 independent runs, these multi-objective algorithms were evaluated against their single-objective counterparts using performance metrics such as the Coefficient of Determination ( $ R^2 $ R 2 ) and Mean Squared Error (MSE). The results demonstrate that multi-objective metaheuristics outperform their single-objective variants, proving an effective alternative for accurate and efficient MIMO system identification. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/17445760.2025.2508174
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 424
    Subjects:
      – SubjectFull: System identification
        Type: general
      – SubjectFull: MIMO systems
        Type: general
      – SubjectFull: Grey Wolf Optimizer algorithm
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Genetic algorithms
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      – SubjectFull: Multi-objective optimization
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      – TitleFull: Multi-objective metaheuristics applied for the multivariable system identification.
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            NameFull: Leandro, G. V.
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            – D: 01
              M: 08
              Text: Aug2025
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              Y: 2025
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            – TitleFull: International Journal of Parallel, Emergent & Distributed Systems
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