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Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling

Most real engineering systems are multivariable systems and multiobjectives in nature, especially in a complex dynamic system. The ultimate objective of dynamic system modeling is to obtain parsimonious and adequate model, where the predictive error and model complexity need to be optimized and sati...

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Main Author: Mohd. Samsuri, Saiful Farhan
Format: Thesis
Language:English
Published: 2022
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/101421/1/SaifulFarhanMohdSamsuriPSKM2022.pdf.pdf
http://eprints.utm.my/id/eprint/101421/
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spelling my.utm.1014212023-06-14T10:14:27Z http://eprints.utm.my/id/eprint/101421/ Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling Mohd. Samsuri, Saiful Farhan TJ Mechanical engineering and machinery Most real engineering systems are multivariable systems and multiobjectives in nature, especially in a complex dynamic system. The ultimate objective of dynamic system modeling is to obtain parsimonious and adequate model, where the predictive error and model complexity need to be optimized and satisfied simultaneously. This study attempts to establish the needs of a multiobjective optimization algorithm by comparing it with a single-objective of the multivariable optimization algorithm. Two different types of optimization techniques are used: (1) elitist the non-dominated sorting genetic algorithm (NSGA-II) for multiobjective optimization and (2) the modified genetic algorithm (MGA) for single-objective optimization. The results showed that advantage of the multiobjective optimization algorithm compared with the single objective optimization algorithm in developing an adequate and parsimonious model for a discrete-time multivariable dynamics system. A new algorithm based on a multiobjective optimization algorithm for model structure selection is proposed namely multivariable multiobjective optimization using hybrid differential evolution (MOHDE). The proposed algorithm was compared with NSGA-II for model selection in dynamic system modeling of multivariable optimization. The study involved simulated and real systems data for comparison in terms of model predictive accuracy and model complexity. The case studies for real systems were considered in this study for investigating the effectiveness of the multivariable proposed algorithm namely Reference Evapotranspiration (ETo) for MISO systems, offshore structure response for SIMO systems and CD-player arm for MIMO systems. The results showed that the proposed algorithm is capable to produce a good and adequate model with a minimal number of terms and a good predictive accuracy with lower error (less than 1%) on average for all study cases where the result shows that MOHDE outperformed NSGA-II. 2022 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/101421/1/SaifulFarhanMohdSamsuriPSKM2022.pdf.pdf Mohd. Samsuri, Saiful Farhan (2022) Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling. PhD thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:151553
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Mohd. Samsuri, Saiful Farhan
Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling
description Most real engineering systems are multivariable systems and multiobjectives in nature, especially in a complex dynamic system. The ultimate objective of dynamic system modeling is to obtain parsimonious and adequate model, where the predictive error and model complexity need to be optimized and satisfied simultaneously. This study attempts to establish the needs of a multiobjective optimization algorithm by comparing it with a single-objective of the multivariable optimization algorithm. Two different types of optimization techniques are used: (1) elitist the non-dominated sorting genetic algorithm (NSGA-II) for multiobjective optimization and (2) the modified genetic algorithm (MGA) for single-objective optimization. The results showed that advantage of the multiobjective optimization algorithm compared with the single objective optimization algorithm in developing an adequate and parsimonious model for a discrete-time multivariable dynamics system. A new algorithm based on a multiobjective optimization algorithm for model structure selection is proposed namely multivariable multiobjective optimization using hybrid differential evolution (MOHDE). The proposed algorithm was compared with NSGA-II for model selection in dynamic system modeling of multivariable optimization. The study involved simulated and real systems data for comparison in terms of model predictive accuracy and model complexity. The case studies for real systems were considered in this study for investigating the effectiveness of the multivariable proposed algorithm namely Reference Evapotranspiration (ETo) for MISO systems, offshore structure response for SIMO systems and CD-player arm for MIMO systems. The results showed that the proposed algorithm is capable to produce a good and adequate model with a minimal number of terms and a good predictive accuracy with lower error (less than 1%) on average for all study cases where the result shows that MOHDE outperformed NSGA-II.
format Thesis
author Mohd. Samsuri, Saiful Farhan
author_facet Mohd. Samsuri, Saiful Farhan
author_sort Mohd. Samsuri, Saiful Farhan
title Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling
title_short Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling
title_full Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling
title_fullStr Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling
title_full_unstemmed Multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling
title_sort multiobjective model structure optimization using hybrid differential evolution for multivariable dynamic system modeling
publishDate 2022
url http://eprints.utm.my/id/eprint/101421/1/SaifulFarhanMohdSamsuriPSKM2022.pdf.pdf
http://eprints.utm.my/id/eprint/101421/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:151553
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score 13.211869

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