Generalized Test Suite for Continuous Dynamic Multi-objective Optimization

Shao, C; Zhao, Q; Shi, Y; Jiang, J

Generalized Test Suite for Continuous Dynamic Multi-objective Optimization

Shao, C

Zhao, Q Shi, Y Jiang, J

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12654 LNCS, pp. 205-217
Issue Date:: 2021-03-31

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Field	Value	Language
dc.contributor.author	Shao, C https://orcid.org/0000-0002-1065-3537
dc.contributor.author	Zhao, Q
dc.contributor.author	Shi, Y
dc.contributor.author	Jiang, J https://orcid.org/0000-0001-5301-7779
dc.date	2021-03-28
dc.date.accessioned	2022-05-26T01:39:17Z
dc.date.available	2022-05-26T01:39:17Z
dc.date.issued	2021-03-31
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12654 LNCS, pp. 205-217
dc.identifier.isbn	9783030720612
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/157708
dc.description.abstract	Dynamic multi-objective optimization (DMO) has recently attracted increasing interest. Suitable benchmark problems are crucial for evaluating the performance of DMO solvers. However, most of the existing DMO benchmarks mainly focus on Pareto-optimal solutions (PS) varying on the hyperplane, which may produce some unexpected bias for algorithmic analysis. Furthermore, they do not comprehensively consider the general time-linkage property, yet which is commonly observed in real-world applications. To alleviate these two issues, we designed a generalized test suite (GTS) for DMO with the following two advantages over previous existing benchmarks: 1) the PS can change on the hypersurface over time, to better compare the tracking ability of different DMO solvers; 2) the general time-linkage feature is included to systemically investigate the algorithmic robustness in the dynamic environment. Experimental results on five representative DMO algorithms demonstrated the proposed GTS can efficiently discriminate the performance of DMO algorithms and is more general than existing benchmarks.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof	11th International Conference on Evolutionary Multi-Criterion Optimization
dc.relation.isbasedon	10.1007/978-3-030-72062-9_17
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Generalized Test Suite for Continuous Dynamic Multi-objective Optimization
dc.type	Conference Proceeding
utslib.citation.volume	12654 LNCS
utslib.location.activity	Shenzhen, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-26T01:39:15Z
pubs.finish-date	2021-03-31
pubs.publication-status	Published online
pubs.start-date	2021-03-28
pubs.volume	12654 LNCS

Abstract:

Dynamic multi-objective optimization (DMO) has recently attracted increasing interest. Suitable benchmark problems are crucial for evaluating the performance of DMO solvers. However, most of the existing DMO benchmarks mainly focus on Pareto-optimal solutions (PS) varying on the hyperplane, which may produce some unexpected bias for algorithmic analysis. Furthermore, they do not comprehensively consider the general time-linkage property, yet which is commonly observed in real-world applications. To alleviate these two issues, we designed a generalized test suite (GTS) for DMO with the following two advantages over previous existing benchmarks: 1) the PS can change on the hypersurface over time, to better compare the tracking ability of different DMO solvers; 2) the general time-linkage feature is included to systemically investigate the algorithmic robustness in the dynamic environment. Experimental results on five representative DMO algorithms demonstrated the proposed GTS can efficiently discriminate the performance of DMO algorithms and is more general than existing benchmarks.

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http://hdl.handle.net/10453/157708