MOEA/D for Multiple Multi-objective Optimization

Jingjing Chen; Qingfu Zhang; Genghui Li

doi:10.1007/978-3-030-72062-9_13

MOEA/D for Multiple Multi-objective Optimization

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

4 Citations (Scopus)

Abstract

This paper defines a new multi-objective optimization problem, called multiple multi-objective optimization problem (MMOP). An MMOP is composed of several multi-objective optimization problems (MOPs) with different decision spaces and the same objective space, and its optimal solutions are non-dominated solutions among Pareto optimal solutions of all the individual MOPs. We construct a set of benchmark test instances with different characteristics. We propose a decomposition-based multi-objective evolutionary algorithm for solving MMOP (MOEA/D-MM). Experimental results on benchmarks show that MOEA/D-MM is more effective than some well-known traditional multi-objective evolutionary algorithms on MMOP.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization
Subtitle of host publication	11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings
Editors	Hisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou
Place of Publication	Cham
Publisher	Springer
Pages	152-163
ISBN (Electronic)	9783030720629
ISBN (Print)	9783030720612
DOIs	https://doi.org/10.1007/978-3-030-72062-9_13
Publication status	Published - 2021
Event	11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021) - Hampton by Hilton Hotel (on-site & on-line), Shenzhen, China Duration: 28 Mar 2021 → 31 Mar 2021

Publication series

Name	Lecture Notes in Computer Science
Volume	12654
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021)
Place	China
City	Shenzhen
Period	28/03/21 → 31/03/21

Research Keywords

MOEA/D
Multi-objective optimization
Multiple multi-objective optimization

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10.1007/978-3-030-72062-9_13

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Chen, J., Zhang, Q., & Li, G. (2021). MOEA/D for Multiple Multi-objective Optimization. In H. Ishibuchi, Q. Zhang, R. Cheng, K. Li, H. Li, H. Wang, & A. Zhou (Eds.), Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 152-163). (Lecture Notes in Computer Science; Vol. 12654). Springer . https://doi.org/10.1007/978-3-030-72062-9_13

Chen, Jingjing ; Zhang, Qingfu ; Li, Genghui. / MOEA/D for Multiple Multi-objective Optimization. Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. editor / Hisao Ishibuchi ; Qingfu Zhang ; Ran Cheng ; Ke Li ; Hui Li ; Handing Wang ; Aimin Zhou. Cham : Springer , 2021. pp. 152-163 (Lecture Notes in Computer Science).

@inproceedings{a8d2161547424deabbded592133f90f1,

title = "MOEA/D for Multiple Multi-objective Optimization",

abstract = "This paper defines a new multi-objective optimization problem, called multiple multi-objective optimization problem (MMOP). An MMOP is composed of several multi-objective optimization problems (MOPs) with different decision spaces and the same objective space, and its optimal solutions are non-dominated solutions among Pareto optimal solutions of all the individual MOPs. We construct a set of benchmark test instances with different characteristics. We propose a decomposition-based multi-objective evolutionary algorithm for solving MMOP (MOEA/D-MM). Experimental results on benchmarks show that MOEA/D-MM is more effective than some well-known traditional multi-objective evolutionary algorithms on MMOP.",

keywords = "MOEA/D, Multi-objective optimization, Multiple multi-objective optimization",

author = "Jingjing Chen and Qingfu Zhang and Genghui Li",

year = "2021",

doi = "10.1007/978-3-030-72062-9\_13",

language = "English",

isbn = "9783030720612",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "152--163",

editor = "Hisao Ishibuchi and Qingfu Zhang and Ran Cheng and Ke Li and Hui Li and Handing Wang and Aimin Zhou",

booktitle = "Evolutionary Multi-Criterion Optimization",

note = "11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021) ; Conference date: 28-03-2021 Through 31-03-2021",

}

Chen, J , Zhang, Q & Li, G 2021, MOEA/D for Multiple Multi-objective Optimization. in H Ishibuchi, Q Zhang, R Cheng, K Li, H Li, H Wang & A Zhou (eds), Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. Lecture Notes in Computer Science, vol. 12654, Springer , Cham, pp. 152-163, 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), Shenzhen, China, 28/03/21. https://doi.org/10.1007/978-3-030-72062-9_13

MOEA/D for Multiple Multi-objective Optimization. / Chen, Jingjing ; Zhang, Qingfu ; Li, Genghui.
Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. ed. / Hisao Ishibuchi; Qingfu Zhang; Ran Cheng; Ke Li; Hui Li; Handing Wang; Aimin Zhou. Cham: Springer , 2021. p. 152-163 (Lecture Notes in Computer Science; Vol. 12654).

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

TY - GEN

T1 - MOEA/D for Multiple Multi-objective Optimization

AU - Chen, Jingjing

AU - Zhang, Qingfu

AU - Li, Genghui

PY - 2021

Y1 - 2021

N2 - This paper defines a new multi-objective optimization problem, called multiple multi-objective optimization problem (MMOP). An MMOP is composed of several multi-objective optimization problems (MOPs) with different decision spaces and the same objective space, and its optimal solutions are non-dominated solutions among Pareto optimal solutions of all the individual MOPs. We construct a set of benchmark test instances with different characteristics. We propose a decomposition-based multi-objective evolutionary algorithm for solving MMOP (MOEA/D-MM). Experimental results on benchmarks show that MOEA/D-MM is more effective than some well-known traditional multi-objective evolutionary algorithms on MMOP.

AB - This paper defines a new multi-objective optimization problem, called multiple multi-objective optimization problem (MMOP). An MMOP is composed of several multi-objective optimization problems (MOPs) with different decision spaces and the same objective space, and its optimal solutions are non-dominated solutions among Pareto optimal solutions of all the individual MOPs. We construct a set of benchmark test instances with different characteristics. We propose a decomposition-based multi-objective evolutionary algorithm for solving MMOP (MOEA/D-MM). Experimental results on benchmarks show that MOEA/D-MM is more effective than some well-known traditional multi-objective evolutionary algorithms on MMOP.

KW - MOEA/D

KW - Multi-objective optimization

KW - Multiple multi-objective optimization

UR - https://www.scopus.com/pages/publications/85107303017

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85107303017&origin=recordpage

U2 - 10.1007/978-3-030-72062-9_13

DO - 10.1007/978-3-030-72062-9_13

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

SN - 9783030720612

T3 - Lecture Notes in Computer Science

SP - 152

EP - 163

BT - Evolutionary Multi-Criterion Optimization

A2 - Ishibuchi, Hisao

A2 - Zhang, Qingfu

A2 - Cheng, Ran

A2 - Li, Ke

A2 - Li, Hui

A2 - Wang, Handing

A2 - Zhou, Aimin

PB - Springer

CY - Cham

T2 - 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021)

Y2 - 28 March 2021 through 31 March 2021

ER -

Chen J , Zhang Q , Li G. MOEA/D for Multiple Multi-objective Optimization. In Ishibuchi H, Zhang Q, Cheng R, Li K, Li H, Wang H, Zhou A, editors, Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. Cham: Springer . 2021. p. 152-163. (Lecture Notes in Computer Science). Epub 2021 Mar 24. doi: 10.1007/978-3-030-72062-9_13

MOEA/D for Multiple Multi-objective Optimization

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