A Comparison of Constraint Handling Techniques on NSGA-II

Hobbie, JG; Gandomi, AH; Rahimi, I

A Comparison of Constraint Handling Techniques on NSGA-II

Hobbie, JG Gandomi, AH Rahimi, I

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Archives of Computational Methods in Engineering, 2021, 28, (5), pp. 3475-3490
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Hobbie, JG
dc.contributor.author	Gandomi, AH
dc.contributor.author	Rahimi, I
dc.date.accessioned	2021-09-12T00:05:29Z
dc.date.available	2021-09-12T00:05:29Z
dc.date.issued	2021-08-01
dc.identifier.citation	Archives of Computational Methods in Engineering, 2021, 28, (5), pp. 3475-3490
dc.identifier.issn	1134-3060
dc.identifier.issn	1886-1784
dc.identifier.uri	http://hdl.handle.net/10453/150478
dc.description.abstract	Almost all real-world and engineering problems involve multi-objective optimization of some sort that is often constrained. To solve these constrained multi-objective optimization problems, constrained multi-objective optimization evolutionary algorithms (CMOEAs) are enlisted. These CMOEAs require specific constraint handling techniques. This study aims to address and test the most successful constraint handling techniques, seven different penalty constraint techniques, as applied to the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In this paper, NSGA-II is chosen because of its high popularity amongst evolutionary algorithms. Inverted Generational Distance and Hypervolume are the main metrics that are discussed to compare the constraint handling techniques. NSGA-II is applied on 13 constrained multi-objective problems known as CF1-CF10, C1-DTLZ1, C2-DTLZ2, and C3-DTLZ4. The result of IGD and HV values are compared and the feasibility proportions of each combination on each problem are shown. The results of simulation present interesting findings that have been presented at the end of paper as discussion and conclusion.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Archives of Computational Methods in Engineering
dc.relation.isbasedon	10.1007/s11831-020-09525-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.title	A Comparison of Constraint Handling Techniques on NSGA-II
dc.type	Journal Article
utslib.citation.volume	28
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2021-09-12T00:05:28Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	28
utslib.citation.issue	5

Abstract:

Almost all real-world and engineering problems involve multi-objective optimization of some sort that is often constrained. To solve these constrained multi-objective optimization problems, constrained multi-objective optimization evolutionary algorithms (CMOEAs) are enlisted. These CMOEAs require specific constraint handling techniques. This study aims to address and test the most successful constraint handling techniques, seven different penalty constraint techniques, as applied to the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In this paper, NSGA-II is chosen because of its high popularity amongst evolutionary algorithms. Inverted Generational Distance and Hypervolume are the main metrics that are discussed to compare the constraint handling techniques. NSGA-II is applied on 13 constrained multi-objective problems known as CF1-CF10, C1-DTLZ1, C2-DTLZ2, and C3-DTLZ4. The result of IGD and HV values are compared and the feasibility proportions of each combination on each problem are shown. The results of simulation present interesting findings that have been presented at the end of paper as discussion and conclusion.

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http://hdl.handle.net/10453/150478