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An active learning strategy of reliability-based design and optimization by parallel adaptive sequential importance candidate region method

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Abstract

Reliability-based design and optimization (RBDO) is increasingly critical for high-quality products. However, existing methods, such as active learning Kriging (ALK) and sequential optimization and reliability assessment (SORA), encounter challenges in efficiently and accurately handling complex nonlinear probabilistic constraints. This paper introduces a strategy for ALK called parallel adaptive candidate region (PACR), which leverages clustering methods (K-means or DBSCAN) inspired by machine learning. The novelty is that this method incorporates advanced techniques for RBDO, including quasi-sequence importance sampling (SIS) with a Markov chain based on quasi-samples obtained by ALK model. Another novelty is that a new learning function for reliability constraints, known as the influence factor function with a limit state constraints g(x) = 0 (IFG), has been used to simplify the calculation loop. The efficiency of the methods is validated through various mathematical tests. Then, a specific high-fidelity case study for a planetary roller screw mechanism (PRSM) is conducted to discover an optimal high-precision design with a reasonable load distribution under uncertain constraints. With our method, the standard deviation of the load sharing coefficient decreases by 35.90%, from 0.6221 to 0.3988, thereby affirming the effectiveness of our proposed approach in relatively complex nonlinear optimization problems with uncertain constraints.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 12302156 and 52305273), Guangdong Basic and Applied Basic Research Foundation (Grant 2022A1515110636), the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2023-JC-QN-0012)，National Science and Technology Major Project (J2019-IV-0017-0085).

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Authors and Affiliations

  1. School of Civil Aviation, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Mengchuang Zhang, Zhizhuo Zhang, Shasha Xia, Xiaochuan Li & Zhiping Yin

  2. Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, 518000, Guangdong, People’s Republic of China

    Mengchuang Zhang

  3. School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou, 215009, Jiangsu, People’s Republic of China

    Qin Yao

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  1. Mengchuang Zhang
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  2. Zhizhuo Zhang
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  4. Xiaochuan Li
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  5. Qin Yao
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  6. Zhiping Yin
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Correspondence to Zhiping Yin.

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Zhang, M., Zhang, Z., Xia, S. et al. An active learning strategy of reliability-based design and optimization by parallel adaptive sequential importance candidate region method. Struct Multidisc Optim 67, 14 (2024). https://doi.org/10.1007/s00158-023-03724-4

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