On the performance of metamodel assisted MOEA/D

Wudong Liu; Qingfu Zhang; Edward Tsang; Cao Liu; Botond Virginas

doi:10.1007/978-3-540-74581-5_60

On the performance of metamodel assisted MOEA/D

Wudong Liu
, Qingfu Zhang
, Edward Tsang
, Cao Liu
, Botond Virginas

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

18 Citations (Scopus)

Abstract

MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilises the idea of problem decomposition to tackle the complexity from multiple objectives. It shows better performance than most nowadays mainstream MOEA methods in various test problems, especially on the quality of solution's distribution in the Pareto set. This paper aims to bring the strength of metamodel into MOEA/D to help the solving of expensive black-box multi-objective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analysed and compared on several test problems, which shows a promising perspective on this method. © Springer-Verlag Berlin Heidelberg 2007.

Original language	English
Title of host publication	Advances in Computation and Intelligence
Subtitle of host publication	Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings
Editors	Lishan Kang, Yong Liu, Sanyou Zeng
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	547-557
ISBN (Electronic)	978-3-540-74581-5
ISBN (Print)	9783540745808
DOIs	https://doi.org/10.1007/978-3-540-74581-5_60
Publication status	Published - 2007
Externally published	Yes
Event	2nd International Symposium on Intelligence Computation and Applications (ISICA 2007) - Wuhan, China Duration: 21 Sept 2007 → 23 Sept 2007

Publication series

Name	Lecture Notes in Computer Science
Volume	4683
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Symposium on Intelligence Computation and Applications (ISICA 2007)
Place	China
City	Wuhan
Period	21/09/07 → 23/09/07

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10.1007/978-3-540-74581-5_60

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Liu, W., Zhang, Q., Tsang, E., Liu, C., & Virginas, B. (2007). On the performance of metamodel assisted MOEA/D. In L. Kang, Y. Liu, & S. Zeng (Eds.), Advances in Computation and Intelligence: Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings (pp. 547-557). (Lecture Notes in Computer Science; Vol. 4683). Springer . https://doi.org/10.1007/978-3-540-74581-5_60

Liu, Wudong ; Zhang, Qingfu ; Tsang, Edward et al. / On the performance of metamodel assisted MOEA/D. Advances in Computation and Intelligence: Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings. editor / Lishan Kang ; Yong Liu ; Sanyou Zeng. Berlin, Heidelberg : Springer , 2007. pp. 547-557 (Lecture Notes in Computer Science).

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title = "On the performance of metamodel assisted MOEA/D",

abstract = "MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilises the idea of problem decomposition to tackle the complexity from multiple objectives. It shows better performance than most nowadays mainstream MOEA methods in various test problems, especially on the quality of solution's distribution in the Pareto set. This paper aims to bring the strength of metamodel into MOEA/D to help the solving of expensive black-box multi-objective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analysed and compared on several test problems, which shows a promising perspective on this method. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.",

author = "Wudong Liu and Qingfu Zhang and Edward Tsang and Cao Liu and Botond Virginas",

year = "2007",

doi = "10.1007/978-3-540-74581-5\_60",

language = "English",

isbn = "9783540745808",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "547--557",

editor = "Kang, \{Lishan \} and Liu, \{Yong \} and Zeng, \{Sanyou \}",

booktitle = "Advances in Computation and Intelligence",

note = "2nd International Symposium on Intelligence Computation and Applications (ISICA 2007) ; Conference date: 21-09-2007 Through 23-09-2007",

}

Liu, W, Zhang, Q, Tsang, E, Liu, C & Virginas, B 2007, On the performance of metamodel assisted MOEA/D. in L Kang, Y Liu & S Zeng (eds), Advances in Computation and Intelligence: Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings. Lecture Notes in Computer Science, vol. 4683, Springer , Berlin, Heidelberg, pp. 547-557, 2nd International Symposium on Intelligence Computation and Applications (ISICA 2007), Wuhan, China, 21/09/07. https://doi.org/10.1007/978-3-540-74581-5_60

On the performance of metamodel assisted MOEA/D. / Liu, Wudong; Zhang, Qingfu; Tsang, Edward et al.
Advances in Computation and Intelligence: Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings. ed. / Lishan Kang; Yong Liu; Sanyou Zeng. Berlin, Heidelberg: Springer , 2007. p. 547-557 (Lecture Notes in Computer Science; Vol. 4683).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - On the performance of metamodel assisted MOEA/D

AU - Liu, Wudong

AU - Zhang, Qingfu

AU - Tsang, Edward

AU - Liu, Cao

AU - Virginas, Botond

PY - 2007

Y1 - 2007

N2 - MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilises the idea of problem decomposition to tackle the complexity from multiple objectives. It shows better performance than most nowadays mainstream MOEA methods in various test problems, especially on the quality of solution's distribution in the Pareto set. This paper aims to bring the strength of metamodel into MOEA/D to help the solving of expensive black-box multi-objective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analysed and compared on several test problems, which shows a promising perspective on this method. © Springer-Verlag Berlin Heidelberg 2007.

AB - MOEA/D is a novel and successful Multi-Objective Evolutionary Algorithms(MOEA) which utilises the idea of problem decomposition to tackle the complexity from multiple objectives. It shows better performance than most nowadays mainstream MOEA methods in various test problems, especially on the quality of solution's distribution in the Pareto set. This paper aims to bring the strength of metamodel into MOEA/D to help the solving of expensive black-box multi-objective problems. Gaussian Random Field Metamodel(GRFM) is chosen as the approximation method. The performance is analysed and compared on several test problems, which shows a promising perspective on this method. © Springer-Verlag Berlin Heidelberg 2007.

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U2 - 10.1007/978-3-540-74581-5_60

DO - 10.1007/978-3-540-74581-5_60

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783540745808

T3 - Lecture Notes in Computer Science

SP - 547

EP - 557

BT - Advances in Computation and Intelligence

A2 - Kang, Lishan

A2 - Liu, Yong

A2 - Zeng, Sanyou

PB - Springer

CY - Berlin, Heidelberg

T2 - 2nd International Symposium on Intelligence Computation and Applications (ISICA 2007)

Y2 - 21 September 2007 through 23 September 2007

ER -

Liu W, Zhang Q, Tsang E, Liu C, Virginas B. On the performance of metamodel assisted MOEA/D. In Kang L, Liu Y, Zeng S, editors, Advances in Computation and Intelligence: Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings. Berlin, Heidelberg: Springer . 2007. p. 547-557. (Lecture Notes in Computer Science). Epub 2007 Aug 26. doi: 10.1007/978-3-540-74581-5_60

On the performance of metamodel assisted MOEA/D

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