On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D

Zhenkun Wang; Jingda Deng; Qingfu Zhang; Qite Yang

doi:10.1007/978-3-030-72062-9_33

On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D

Zhenkun Wang
, Jingda Deng^*
, Qingfu Zhang
, Qite Yang

^*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) decomposes an multiobjective optimization problem into a number of single-objective subproblems and solves them in a cooperative manner. The subproblems can be designed by various scalarization methods, e.g., the weighted sum (WS) method, the Tchebycheff (TCH) method, and the penalty-based boundary intersection (PBI) method. In this paper, we investigate the PBI method with different parameter settings, and propose a way to set the parameter appropriately. Experimental results suggest that the PBI method with our proposed parameter setting works very well.

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	413-423
ISBN (Electronic)	9783030720629
ISBN (Print)	9783030720612
DOIs	https://doi.org/10.1007/978-3-030-72062-9_33
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
Penalty-based boundary intersection method
Scalarization method

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10.1007/978-3-030-72062-9_33

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Wang, Z., Deng, J., Zhang, Q., & Yang, Q. (2021). On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D. 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. 413-423). (Lecture Notes in Computer Science; Vol. 12654). Springer . https://doi.org/10.1007/978-3-030-72062-9_33

Wang, Zhenkun ; Deng, Jingda ; Zhang, Qingfu et al. / On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D. 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. 413-423 (Lecture Notes in Computer Science).

@inproceedings{89d2132bdd5845968685a54fba712b78,

title = "On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D",

abstract = "Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) decomposes an multiobjective optimization problem into a number of single-objective subproblems and solves them in a cooperative manner. The subproblems can be designed by various scalarization methods, e.g., the weighted sum (WS) method, the Tchebycheff (TCH) method, and the penalty-based boundary intersection (PBI) method. In this paper, we investigate the PBI method with different parameter settings, and propose a way to set the parameter appropriately. Experimental results suggest that the PBI method with our proposed parameter setting works very well.",

keywords = "MOEA/D, Penalty-based boundary intersection method, Scalarization method",

author = "Zhenkun Wang and Jingda Deng and Qingfu Zhang and Qite Yang",

year = "2021",

doi = "10.1007/978-3-030-72062-9\_33",

language = "English",

isbn = "9783030720612",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "413--423",

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",

}

Wang, Z, Deng, J , Zhang, Q & Yang, Q 2021, On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D. 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. 413-423, 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_33

On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D. / Wang, Zhenkun; Deng, Jingda ; Zhang, Qingfu et al.
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. 413-423 (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 - On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D

AU - Wang, Zhenkun

AU - Deng, Jingda

AU - Zhang, Qingfu

AU - Yang, Qite

PY - 2021

Y1 - 2021

N2 - Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) decomposes an multiobjective optimization problem into a number of single-objective subproblems and solves them in a cooperative manner. The subproblems can be designed by various scalarization methods, e.g., the weighted sum (WS) method, the Tchebycheff (TCH) method, and the penalty-based boundary intersection (PBI) method. In this paper, we investigate the PBI method with different parameter settings, and propose a way to set the parameter appropriately. Experimental results suggest that the PBI method with our proposed parameter setting works very well.

AB - Multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) decomposes an multiobjective optimization problem into a number of single-objective subproblems and solves them in a cooperative manner. The subproblems can be designed by various scalarization methods, e.g., the weighted sum (WS) method, the Tchebycheff (TCH) method, and the penalty-based boundary intersection (PBI) method. In this paper, we investigate the PBI method with different parameter settings, and propose a way to set the parameter appropriately. Experimental results suggest that the PBI method with our proposed parameter setting works very well.

KW - MOEA/D

KW - Penalty-based boundary intersection method

KW - Scalarization method

UR - https://www.scopus.com/pages/publications/85107278930

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85107278930&origin=recordpage

U2 - 10.1007/978-3-030-72062-9_33

DO - 10.1007/978-3-030-72062-9_33

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

SN - 9783030720612

T3 - Lecture Notes in Computer Science

SP - 413

EP - 423

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 -

Wang Z, Deng J , Zhang Q, Yang Q. On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D. 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. 413-423. (Lecture Notes in Computer Science). Epub 2021 Mar 24. doi: 10.1007/978-3-030-72062-9_33

On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D

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