Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators. Initially, a set of parameter values was tuned vi...

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Main Authors: Zakaria, Mohd. Zakimi, Jamaluddin, Hishamuddin, Ahmad, Robiah, Harun, Azmi, Hussin, Radhwan, Mohd. Khalil, Ahmad Nabil, Md. Naim, Muhammad Khairy, Annuar, Ahmad Faizal
Format: Article
Language:English
Published: Penerbit UTM Press 2015
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Online Access:http://eprints.utm.my/id/eprint/58814/1/HishamuddinJamaluddin2015_PerturbationParametersTuningofMultiObjective.pdf
http://eprints.utm.my/id/eprint/58814/
http://dx.doi.org/10.11113/jt.v75.5335
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.588142021-12-19T02:54:40Z http://eprints.utm.my/id/eprint/58814/ Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling Zakaria, Mohd. Zakimi Jamaluddin, Hishamuddin Ahmad, Robiah Harun, Azmi Hussin, Radhwan Mohd. Khalil, Ahmad Nabil Md. Naim, Muhammad Khairy Annuar, Ahmad Faizal TJ Mechanical engineering and machinery This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators. Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity. One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters. Penerbit UTM Press 2015-09-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/58814/1/HishamuddinJamaluddin2015_PerturbationParametersTuningofMultiObjective.pdf Zakaria, Mohd. Zakimi and Jamaluddin, Hishamuddin and Ahmad, Robiah and Harun, Azmi and Hussin, Radhwan and Mohd. Khalil, Ahmad Nabil and Md. Naim, Muhammad Khairy and Annuar, Ahmad Faizal (2015) Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. Jurnal Teknologi, 75 (11). pp. 77-90. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v75.5335 DOI:10.11113/jt.v75.5335
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
Zakaria, Mohd. Zakimi
Jamaluddin, Hishamuddin
Ahmad, Robiah
Harun, Azmi
Hussin, Radhwan
Mohd. Khalil, Ahmad Nabil
Md. Naim, Muhammad Khairy
Annuar, Ahmad Faizal
Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
description This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators. Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity. One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.
format Article
author Zakaria, Mohd. Zakimi
Jamaluddin, Hishamuddin
Ahmad, Robiah
Harun, Azmi
Hussin, Radhwan
Mohd. Khalil, Ahmad Nabil
Md. Naim, Muhammad Khairy
Annuar, Ahmad Faizal
author_facet Zakaria, Mohd. Zakimi
Jamaluddin, Hishamuddin
Ahmad, Robiah
Harun, Azmi
Hussin, Radhwan
Mohd. Khalil, Ahmad Nabil
Md. Naim, Muhammad Khairy
Annuar, Ahmad Faizal
author_sort Zakaria, Mohd. Zakimi
title Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
title_short Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
title_full Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
title_fullStr Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
title_full_unstemmed Perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
title_sort perturbation parameters tuning of multi-objective optimization differential evolution and its application to dynamic system modeling
publisher Penerbit UTM Press
publishDate 2015
url http://eprints.utm.my/id/eprint/58814/1/HishamuddinJamaluddin2015_PerturbationParametersTuningofMultiObjective.pdf
http://eprints.utm.my/id/eprint/58814/
http://dx.doi.org/10.11113/jt.v75.5335
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