Multi-objective metaheuristics applied for the multivariable system identification.
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| Title: | Multi-objective metaheuristics applied for the multivariable system identification. |
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| 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] |
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| Database: | Engineering Source |
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