A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization

Rahimi, I; Gandomi, AH; Chen, F; Mezura-Montes, E

A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization

Rahimi, I Gandomi, AH Chen, F

Mezura-Montes, E

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Archives of Computational Methods in Engineering, 2022
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Rahimi, I
dc.contributor.author	Gandomi, AH
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Mezura-Montes, E
dc.date.accessioned	2023-01-20T06:44:24Z
dc.date.available	2023-01-20T06:44:24Z
dc.date.issued	2022-01-01
dc.identifier.citation	Archives of Computational Methods in Engineering, 2022
dc.identifier.issn	1134-3060
dc.identifier.issn	1886-1784
dc.identifier.uri	http://hdl.handle.net/10453/165313
dc.description.abstract	Most real-world problems involve some type of optimization problems that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary optimization in many fields, including theory and application. This presented study provides a novel analysis of scholarly literature on constraint-handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals and articles. As a contribution to this study, the paper reviews the main ideas of the most state-of-the-art constraint handling techniques in population-based optimization, and then the study addresses the bibliometric analysis, with a focus on multi-objective, in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2021 for analysis. The results indicate that the constraint-handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence. Additionally, “Engineering,” “Computer Science,” and “ Mathematics” were identified as the top three research fields in which future research work is anticipated to increase.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Archives of Computational Methods in Engineering
dc.relation.isbasedon	10.1007/s11831-022-09859-9
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.title	A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
dc.type	Journal Article
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	open_access	*
dc.date.updated	2023-01-20T06:44:22Z
pubs.publication-status	Published

Abstract:

Most real-world problems involve some type of optimization problems that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary optimization in many fields, including theory and application. This presented study provides a novel analysis of scholarly literature on constraint-handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals and articles. As a contribution to this study, the paper reviews the main ideas of the most state-of-the-art constraint handling techniques in population-based optimization, and then the study addresses the bibliometric analysis, with a focus on multi-objective, in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2021 for analysis. The results indicate that the constraint-handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence. Additionally, “Engineering,” “Computer Science,” and “ Mathematics” were identified as the top three research fields in which future research work is anticipated to increase.

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http://hdl.handle.net/10453/165313