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Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design

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  • Yu Wang
  • Xiongqing Yu
  • Xiaoping Du

Abstract

A new reliability-based design optimization (RBDO) method based on support vector machines (SVM) and the Most Probable Point (MPP) is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS) is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA) is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.

Suggested Citation

  • Yu Wang & Xiongqing Yu & Xiaoping Du, 2015. "Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, September.
    • Handle: RePEc:hin:jnlmpe:569016
    DOI: 10.1155/2015/569016
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    Cited by:

    1. Yuan, Xiukai & Qian, Yugeng & Chen, Jingqiang & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2023. "Global failure probability function estimation based on an adaptive strategy and combination algorithm," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Ma, Yuan-Zhuo & Jin, Xiang-Xiang & Wu, Xi-Long & Xu, Chang & Li, Hong-Shuang & Zhao, Zhen-Zhou, 2023. "Reliability-based design optimization using adaptive Kriging-A single-loop strategy and a double-loop one," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Xu, Yanwen & Renteria, Anabel & Wang, Pingfeng, 2022. "Adaptive surrogate models with partially observed information," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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