Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization
Hybrid reliability-based design optimization (HRBDO) can provide an effective way to obtain the optimum design in the presence of both random and interval variables. HRBDO is typically described as a nested optimization model. It is computationally expensive when directly solving the HRBDO problem b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10098573/ |
| _version_ | 1797805956198825984 |
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| author | Shengli Liu Xingdong Wang Jianyi Kong Jiabo Zhang Wei Tang |
| author_facet | Shengli Liu Xingdong Wang Jianyi Kong Jiabo Zhang Wei Tang |
| author_sort | Shengli Liu |
| collection | DOAJ |
| description | Hybrid reliability-based design optimization (HRBDO) can provide an effective way to obtain the optimum design in the presence of both random and interval variables. HRBDO is typically described as a nested optimization model. It is computationally expensive when directly solving the HRBDO problem by the nested optimization method. To address this issue, this paper develops an efficient decoupled HRBDO method that aims at performance measure function approximation by the adaptive Kriging model (KPMFA). The proposed KPMFA method includes three main blocks, namely hybrid inverse reliability analysis, performance measure function approximation, and equivalent deterministic optimization. In KPMFA, the adaptation of the adaptive chaos control (ACC) algorithm for inverse reliability analysis that accommodates interval variables is developed. Moreover, an adaptive strategy with two-stage of enrichment for the Kriging model is developed to approximate performance measure functions on the region of interest. Then, the optimization can be proceeded using the Kriging model of performance measure functions. Finally, five illustrative HRBDO problems are investigated to demonstrate the accuracy and efficiency of the proposed KPMFA method. |
| first_indexed | 2024-03-13T06:00:27Z |
| format | Article |
| id | doaj.art-ba58a1e8d8a9431c94eb6a50e0149101 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:00:27Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ba58a1e8d8a9431c94eb6a50e01491012023-06-12T23:01:17ZengIEEEIEEE Access2169-35362023-01-0111473394735010.1109/ACCESS.2023.326614010098573Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design OptimizationShengli Liu0https://orcid.org/0000-0002-2883-1258Xingdong Wang1https://orcid.org/0000-0002-9432-6399Jianyi Kong2Jiabo Zhang3Wei Tang4Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan, ChinaBeijing Spacecrafts, China Aerospace Science and Technology Corporation, Beijing, ChinaKey Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan, ChinaHybrid reliability-based design optimization (HRBDO) can provide an effective way to obtain the optimum design in the presence of both random and interval variables. HRBDO is typically described as a nested optimization model. It is computationally expensive when directly solving the HRBDO problem by the nested optimization method. To address this issue, this paper develops an efficient decoupled HRBDO method that aims at performance measure function approximation by the adaptive Kriging model (KPMFA). The proposed KPMFA method includes three main blocks, namely hybrid inverse reliability analysis, performance measure function approximation, and equivalent deterministic optimization. In KPMFA, the adaptation of the adaptive chaos control (ACC) algorithm for inverse reliability analysis that accommodates interval variables is developed. Moreover, an adaptive strategy with two-stage of enrichment for the Kriging model is developed to approximate performance measure functions on the region of interest. Then, the optimization can be proceeded using the Kriging model of performance measure functions. Finally, five illustrative HRBDO problems are investigated to demonstrate the accuracy and efficiency of the proposed KPMFA method.https://ieeexplore.ieee.org/document/10098573/Hybrid reliability-based design optimization (HRBDO)random and interval variablesadaptive kriging modelperformance measure function approximation |
| spellingShingle | Shengli Liu Xingdong Wang Jianyi Kong Jiabo Zhang Wei Tang Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization IEEE Access Hybrid reliability-based design optimization (HRBDO) random and interval variables adaptive kriging model performance measure function approximation |
| title | Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization |
| title_full | Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization |
| title_fullStr | Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization |
| title_full_unstemmed | Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization |
| title_short | Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization |
| title_sort | kriging based performance measure function approximation method for hybrid reliability based design optimization |
| topic | Hybrid reliability-based design optimization (HRBDO) random and interval variables adaptive kriging model performance measure function approximation |
| url | https://ieeexplore.ieee.org/document/10098573/ |
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