Publication:
Parameter Tuning for Comparing Multi-Objective Evolutionary Algorithms Applied to System Identification Problems

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cris.author.scopus-author-id 35109871000
cris.author.scopus-author-id 57200986655
cris.author.scopus-author-id 36999011800
cris.author.scopus-author-id 57219403616
dc.contributor.author Samsuri S.F.M.
dc.contributor.author Ahmad R.
dc.contributor.author Zakaria M.Z.
dc.contributor.author Zain M.Z.M.
dc.date.accessioned 2024-12-11T07:43:52Z
dc.date.available 2024-12-11T07:43:52Z
dc.date.issued 2019-08-01
dc.description.abstract The parameter estimation tuning is one of the procedures of system identification. One of the identification problems is to find an optimal model structure especially for representing the complex multivariable dynamic systems. Therefore, this problem needs to be solved by using multi-objective optimization with two objective functions namely minimum predictive error and model complexity. In this paper, the tuning of control parameters is studied before comparing multi-objective evolutionary algorithms. The framework is based on the system identification problems. In parameter tuning, two main qualitative parameters of evolutionary algorithms are focused on crossover rate and mutation rate. The multi-objective evolutionary algorithms used for this study are an elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective optimization using differential evolution (MOODE). The different parameter settings are compared in order to obtain good parameter setting for NSGA-II and MOODE. Then, the performance for both algorithms is also compared using Pareto front. The plotted graphs of two objective functions show the non-dominated fronts achieved by the algorithms. The results proved that MOODE shows an advantage in solving system identification problems in a multivariable nonlinear dynamic system.
dc.identifier.doi 10.1109/ICSIMA47653.2019.9057333
dc.identifier.isbn [9781728139524]
dc.identifier.scopus 2-s2.0-85083915908
dc.identifier.uri https://hdl.handle.net/20.500.14170/10090
dc.relation.funding Universiti Teknologi Malaysia
dc.relation.grantno 130000.7840.4F850
dc.relation.ispartof 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Application, ICSIMA 2019
dc.relation.ispartofseries 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Application, ICSIMA 2019
dc.subject differential evolutionary algorithm | multi-objective optimization | system identification | Tuning parameter
dc.title Parameter Tuning for Comparing Multi-Objective Evolutionary Algorithms Applied to System Identification Problems
dc.type Conference Proceeding
dspace.entity.type Publication
oairecerif.affiliation.orgunit Universiti Teknologi Malaysia
oairecerif.affiliation.orgunit Razak Faculty of Technology and Informatics
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Teknologi Malaysia
oairecerif.citation.number 9057333
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person.identifier.scopus-author-id 35109871000
person.identifier.scopus-author-id 57200986655
person.identifier.scopus-author-id 36999011800
person.identifier.scopus-author-id 57219403616
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