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Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems

Modeling input–output data representing a dynamic system is a challenging task when multiple objectives are involved. The developed model needs to be parsimonious yet still adequate. To achieve these goals, two objective functions, i.e. optimum structure and minimum predictive error, need to be sati...

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Main Authors:	Zakaria, Mohd. Zakimi, Jamaluddin, Hishamuddin, Ahmad, Robiah, Loghmanian, Sayed Mohammad Reza
Format:	Article
Published:	2012
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.utm.my/id/eprint/46708/ http://dx.doi.org/10.1177/0959651812439969
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id	my.utm.46708
record_format	eprints
spelling	my.utm.467082017-09-18T04:37:45Z http://eprints.utm.my/id/eprint/46708/ Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems Zakaria, Mohd. Zakimi Jamaluddin, Hishamuddin Ahmad, Robiah Loghmanian, Sayed Mohammad Reza TJ Mechanical engineering and machinery Modeling input–output data representing a dynamic system is a challenging task when multiple objectives are involved. The developed model needs to be parsimonious yet still adequate. To achieve these goals, two objective functions, i.e. optimum structure and minimum predictive error, need to be satisfied. Most works in system identification only consider one objective function, i.e. minimum predictive error, and the model structure is obtained by trial and error. This paper attempts to establish the needs of a multi-objective optimization algorithm by comparing it with a single-objective optimization algorithm. In this study, two different types of optimization algorithms are used to model a discrete-time system. These are an elitist non-dominated sorting genetic algorithm for multi-objective optimization and a modified genetic algorithm for single-objective optimization. Simulated and real systems data are studied for comparison in terms of model predictive accuracy and model complexity. The results show the advantage of the multi-objective optimization algorithm compared with the single-objective optimization algorithm in developing an adequate and parsimonious model for a discrete-time system. 2012 Article PeerReviewed Zakaria, Mohd. Zakimi and Jamaluddin, Hishamuddin and Ahmad, Robiah and Loghmanian, Sayed Mohammad Reza (2012) Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 226 (7). pp. 994-1005. ISSN 0959-6518 http://dx.doi.org/10.1177/0959651812439969 10.1177/0959651812439969
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/
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Zakaria, Mohd. Zakimi Jamaluddin, Hishamuddin Ahmad, Robiah Loghmanian, Sayed Mohammad Reza Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
description	Modeling input–output data representing a dynamic system is a challenging task when multiple objectives are involved. The developed model needs to be parsimonious yet still adequate. To achieve these goals, two objective functions, i.e. optimum structure and minimum predictive error, need to be satisfied. Most works in system identification only consider one objective function, i.e. minimum predictive error, and the model structure is obtained by trial and error. This paper attempts to establish the needs of a multi-objective optimization algorithm by comparing it with a single-objective optimization algorithm. In this study, two different types of optimization algorithms are used to model a discrete-time system. These are an elitist non-dominated sorting genetic algorithm for multi-objective optimization and a modified genetic algorithm for single-objective optimization. Simulated and real systems data are studied for comparison in terms of model predictive accuracy and model complexity. The results show the advantage of the multi-objective optimization algorithm compared with the single-objective optimization algorithm in developing an adequate and parsimonious model for a discrete-time system.
format	Article
author	Zakaria, Mohd. Zakimi Jamaluddin, Hishamuddin Ahmad, Robiah Loghmanian, Sayed Mohammad Reza
author_facet	Zakaria, Mohd. Zakimi Jamaluddin, Hishamuddin Ahmad, Robiah Loghmanian, Sayed Mohammad Reza
author_sort	Zakaria, Mohd. Zakimi
title	Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
title_short	Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
title_full	Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
title_fullStr	Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
title_full_unstemmed	Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
title_sort	comparison between multi-objective and single-objective optimization for the modeling of dynamic systems
publishDate	2012
url	http://eprints.utm.my/id/eprint/46708/ http://dx.doi.org/10.1177/0959651812439969
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score	13.211869

Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems

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