Constrained differential evolution using generalized opposition-based learning

Wei, W; Zhou, J; Chen, F; Yuan, H

Constrained differential evolution using generalized opposition-based learning

Wei, W Zhou, J

Chen, F

Yuan, H

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Soft Computing, 2016, 20, (11), pp. 4413-4437
Issue Date:: 2016-11-01

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Field	Value	Language
dc.contributor.author	Wei, W
dc.contributor.author	Zhou, J https://orcid.org/0000-0001-6034-644X
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Yuan, H
dc.date.accessioned	2022-08-01T20:28:32Z
dc.date.available	2022-08-01T20:28:32Z
dc.date.issued	2016-11-01
dc.identifier.citation	Soft Computing, 2016, 20, (11), pp. 4413-4437
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/159460
dc.description.abstract	Differential evolution (DE) is a well-known optimization approach to deal with nonlinear and complex optimization problems. However, many real-world optimization problems are constrained problems that involve equality and inequality constraints. DE with constraint handling techniques, named constrained differential evolution (CDE), can be used to solve constrained optimization problems. In this paper, we propose a new CDE framework that uses generalized opposition-based learning (GOBL), named GOBL-CDE. In GOBL-CDE, firstly, the transformed population is generated using general opposition-based learning in the population initialization. Secondly, the transformed population and the initial population are merged and only half of the best individuals are selected to compose the new initial population to proceed mutation, crossover, and selection. Lastly, based on a jumping probability, the transformed population is calculated again after generating new populations, and the fittest individuals are selected to compose new population from the union of the current population and the transformed population. The GOBL-CDE framework can be applied to most CDE variants. As examples, in this study, the framework is applied to two popular representative CDE variants, i.e., rank-iMDDE and εDEag. Experiment results on 24 benchmark functions from CEC’2006 and 18 benchmark functions from CEC’2010 show that the proposed framework is an effective approach to enhance the performance of CDE algorithms.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-015-2001-1
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Constrained differential evolution using generalized opposition-based learning
dc.type	Journal Article
utslib.citation.volume	20
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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	2022-08-01T20:28:30Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	11

Abstract:

Differential evolution (DE) is a well-known optimization approach to deal with nonlinear and complex optimization problems. However, many real-world optimization problems are constrained problems that involve equality and inequality constraints. DE with constraint handling techniques, named constrained differential evolution (CDE), can be used to solve constrained optimization problems. In this paper, we propose a new CDE framework that uses generalized opposition-based learning (GOBL), named GOBL-CDE. In GOBL-CDE, firstly, the transformed population is generated using general opposition-based learning in the population initialization. Secondly, the transformed population and the initial population are merged and only half of the best individuals are selected to compose the new initial population to proceed mutation, crossover, and selection. Lastly, based on a jumping probability, the transformed population is calculated again after generating new populations, and the fittest individuals are selected to compose new population from the union of the current population and the transformed population. The GOBL-CDE framework can be applied to most CDE variants. As examples, in this study, the framework is applied to two popular representative CDE variants, i.e., rank-iMDDE and εDEag. Experiment results on 24 benchmark functions from CEC’2006 and 18 benchmark functions from CEC’2010 show that the proposed framework is an effective approach to enhance the performance of CDE algorithms.

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http://hdl.handle.net/10453/159460