Behavior of crossover operators in NSGA-III for large-scale optimization problems

Yi, JH; Xing, LN; Wang, GG; Dong, J; Vasilakos, AV; Alavi, AH; Wang, L

Behavior of crossover operators in NSGA-III for large-scale optimization problems

Yi, JH Xing, LN Wang, GG Dong, J Vasilakos, AV Alavi, AH Wang, L

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2020, 509, pp. 470-487
Issue Date:: 2020-01-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0020025518308016-main.pdf	Published version	1.06 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yi, JH
dc.contributor.author	Xing, LN
dc.contributor.author	Wang, GG
dc.contributor.author	Dong, J
dc.contributor.author	Vasilakos, AV
dc.contributor.author	Alavi, AH
dc.contributor.author	Wang, L
dc.date.accessioned	2020-11-05T20:35:32Z
dc.date.available	2020-11-05T20:35:32Z
dc.date.issued	2020-01-01
dc.identifier.citation	Information Sciences, 2020, 509, pp. 470-487
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/143787
dc.description.abstract	© 2018 Elsevier Inc. Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2018.10.005
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Behavior of crossover operators in NSGA-III for large-scale optimization problems
dc.type	Journal Article
utslib.citation.volume	509
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2020-11-05T20:35:22Z
pubs.publication-status	Published
pubs.volume	509

Abstract:

© 2018 Elsevier Inc. Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/143787