A replacement strategy for balancing convergence and diversity in MOEA/D

Zhenkun Wang; Qingfu Zhang; Maoguo Gong; Aimin Zhou

doi:10.1109/CEC.2014.6900319

A replacement strategy for balancing convergence and diversity in MOEA/D

Zhenkun Wang
, Qingfu Zhang
, Maoguo Gong
, Aimin Zhou

Department of Computer Science

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

50 Citations (Scopus)

Abstract

This paper studies the replacement schemes in MOEA/D and proposes a new replacement named global replacement. It can improve the performance of MOEA/D. Moreover, trade-offs between convergence and diversity can be easily controlled in this replacement strategy. It also shows that different problems need different trade-offs between convergence and diversity. We test the MOEA/D with this global replacement on three sets of benchmark problems to demonstrate its effectiveness.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	IEEE
Pages	2132-2139
ISBN (Print)	9781479914883
DOIs	https://doi.org/10.1109/CEC.2014.6900319
Publication status	Published - Jul 2014
Event	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Conference

Conference	2014 IEEE Congress on Evolutionary Computation, CEC 2014
Place	China
City	Beijing
Period	6/07/14 → 11/07/14

Research Keywords

MOEA/D
Multiobjective optimization
replacement
selection operator

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10.1109/CEC.2014.6900319

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title = "A replacement strategy for balancing convergence and diversity in MOEA/D",

abstract = "This paper studies the replacement schemes in MOEA/D and proposes a new replacement named global replacement. It can improve the performance of MOEA/D. Moreover, trade-offs between convergence and diversity can be easily controlled in this replacement strategy. It also shows that different problems need different trade-offs between convergence and diversity. We test the MOEA/D with this global replacement on three sets of benchmark problems to demonstrate its effectiveness.",

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language = "English",

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pages = "2132--2139",

booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

publisher = "IEEE",

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note = "2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

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A replacement strategy for balancing convergence and diversity in MOEA/D. / Wang, Zhenkun; Zhang, Qingfu; Gong, Maoguo et al.
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. IEEE, 2014. p. 2132-2139 6900319.

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

TY - GEN

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AU - Zhang, Qingfu

AU - Gong, Maoguo

AU - Zhou, Aimin

PY - 2014/7

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N2 - This paper studies the replacement schemes in MOEA/D and proposes a new replacement named global replacement. It can improve the performance of MOEA/D. Moreover, trade-offs between convergence and diversity can be easily controlled in this replacement strategy. It also shows that different problems need different trade-offs between convergence and diversity. We test the MOEA/D with this global replacement on three sets of benchmark problems to demonstrate its effectiveness.

AB - This paper studies the replacement schemes in MOEA/D and proposes a new replacement named global replacement. It can improve the performance of MOEA/D. Moreover, trade-offs between convergence and diversity can be easily controlled in this replacement strategy. It also shows that different problems need different trade-offs between convergence and diversity. We test the MOEA/D with this global replacement on three sets of benchmark problems to demonstrate its effectiveness.

KW - MOEA/D

KW - Multiobjective optimization

KW - replacement

KW - selection operator

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

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EP - 2139

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - IEEE

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

A replacement strategy for balancing convergence and diversity in MOEA/D

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