Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems
The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In th...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/698453 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567517302751232 |
|---|---|
| author | Debiao Meng Xiaoling Zhang Hong-Zhong Huang Zhonglai Wang Huanwei Xu |
| author_facet | Debiao Meng Xiaoling Zhang Hong-Zhong Huang Zhonglai Wang Huanwei Xu |
| author_sort | Debiao Meng |
| collection | DOAJ |
| description | The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO. |
| format | Article |
| id | doaj-art-5e4645ba2f0f438599cf0b2fe981cabb |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-5e4645ba2f0f438599cf0b2fe981cabb2025-02-03T01:01:16ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/698453698453Interaction Prediction Optimization in Multidisciplinary Design Optimization ProblemsDebiao Meng0Xiaoling Zhang1Hong-Zhong Huang2Zhonglai Wang3Huanwei Xu4School of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, ChinaSchool of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, ChinaSchool of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, ChinaSchool of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, ChinaSchool of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, ChinaThe distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.http://dx.doi.org/10.1155/2014/698453 |
| spellingShingle | Debiao Meng Xiaoling Zhang Hong-Zhong Huang Zhonglai Wang Huanwei Xu Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems The Scientific World Journal |
| title | Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems |
| title_full | Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems |
| title_fullStr | Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems |
| title_full_unstemmed | Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems |
| title_short | Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems |
| title_sort | interaction prediction optimization in multidisciplinary design optimization problems |
| url | http://dx.doi.org/10.1155/2014/698453 |
| work_keys_str_mv | AT debiaomeng interactionpredictionoptimizationinmultidisciplinarydesignoptimizationproblems AT xiaolingzhang interactionpredictionoptimizationinmultidisciplinarydesignoptimizationproblems AT hongzhonghuang interactionpredictionoptimizationinmultidisciplinarydesignoptimizationproblems AT zhonglaiwang interactionpredictionoptimizationinmultidisciplinarydesignoptimizationproblems AT huanweixu interactionpredictionoptimizationinmultidisciplinarydesignoptimizationproblems |