On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| 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 |
| Publication status | Published - 2021 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 12654 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Conference
| Title | 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021) |
|---|---|
| Location | Hampton by Hilton Hotel (on-site & on-line) |
| Place | China |
| City | Shenzhen |
| Period | 28 - 31 March 2021 |
Link(s)
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.
Research Area(s)
- MOEA/D, Penalty-based boundary intersection method, Scalarization method
Citation Format(s)
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).
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
