Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study

Behmanesh, R; Rahimi, I; Gandomi, AH

Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study

Behmanesh, R Rahimi, I Gandomi, AH

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Archives of Computational Methods in Engineering, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Behmanesh, R
dc.contributor.author	Rahimi, I
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2021-02-02T04:49:23Z
dc.date.available	2021-02-02T04:49:23Z
dc.date.issued	2020-01-01
dc.identifier.citation	Archives of Computational Methods in Engineering, 2020
dc.identifier.issn	1134-3060
dc.identifier.issn	1886-1784
dc.identifier.uri	http://hdl.handle.net/10453/145731
dc.description.abstract	© 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Archives of Computational Methods in Engineering
dc.relation.isbasedon	10.1007/s11831-020-09415-3
dc.rights	info:eu-repo/semantics/openAccess
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	Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study
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/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-02-02T04:49:16Z
pubs.publication-status	Published

Abstract:

© 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.

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http://hdl.handle.net/10453/145731