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A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems

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  • Yang, Meide
  • Zhang, Dequan
  • Jiang, Chao
  • Han, Xu
  • Li, Qing

Abstract

The study proposed a hybrid adaptive Kriging-based single loop approach (HAK-SLA) to prevent the conventional single loop approach (SLA) from being less efficient, converging to an inaccurate solution or encountering significant difficulty in convergence when dealing with complex RBDO problems with highly nonlinear constraints. The procedural strategy here is to apply surrogate modeling approach in SLA, in which the constraints were surrogated specifically by Kriging models updated using most probable points (MPPs) in each loop. Since the MPPs obtained directly from the Kriging-based SLA may be insufficiently accurate, two different enhanced chaos control methods are developed here to obtain robustly accurate MPPs. Nonetheless, searching for accurate MPPs would increase considerable computational cost; thus, a criterion combining Karush-Kuhn-Tucker (KKT) optimality condition of performance measure approach (PMA) with Kriging model is utilized to determine whether it is necessary to update the Kriging models using accurate MPPs. When the criterion is satisfied, the accurate MPPs are then used to update Kriging models. In this study, performance of the proposed HAK-SLA is demonstrated by comparing with the other conventional RBDO methods through five illustrative RBDO examples. The results exhibit that the proposed approach is of high efficiency and robustness in solving complex RBDO problems.

Suggested Citation

  • Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021002696
    DOI: 10.1016/j.ress.2021.107736
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    Cited by:

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    7. Van Huynh, Thu & Tangaramvong, Sawekchai & Do, Bach & Gao, Wei & Limkatanyu, Suchart, 2023. "Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
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    12. Song, Zhouzhou & Zhang, Hanyu & Liu, Zhao & Zhu, Ping, 2023. "A two-stage Kriging estimation variance reduction method for efficient time-variant reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    13. Zhao, Zhao & Zhao, Yan-Gang & Li, Pei-Pei, 2023. "A novel decoupled time-variant reliability-based design optimization approach by improved extreme value moment method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    14. Li, Guosheng & Ma, Shuaichao & Zhang, Dequan & Yang, Leping & Zhang, Weihua & Wu, Zeping, 2024. "An efficient sequential anisotropic RBF reliability analysis method with fast cross-validation and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Nan, Hang & Liang, Hao & Di, Haoyuan & Li, Hongshuang, 2024. "A gradient-assisted learning strategy of Kriging model for robust design optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    16. Zhang, Dequan & Shen, Shuoshuo & Wu, Jinhui & Wang, Fang & Han, Xu, 2023. "Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    17. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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