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Efficient approach for reliability-based optimization based on weighted importance sampling approach

机译:基于加权重要性抽样方法的基于可靠性的高效优化方法

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摘要

An efficient methodology is presented to perform the reliability-based optimization (RBO). It is based on an efficient weighted approach for constructing an approximation of the failure probability as an explicit function of the design variables which is referred to as the 'failure probability function (FPF)'. It expresses the FPF as a weighted sum of sample values obtained in the simulation-based reliability analysis. The required computational effort for decoupling in each iteration is just single reliability analysis. After the approximation of the FPF is established, the target RBO problem can be decoupled into a deterministic one. Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework. Engineering examples are given to demonstrate the efficiency and accuracy of the presented methodology.
机译:提出了一种有效的方法来执行基于可靠性的优化(RBO)。它基于有效的加权方法,用于将故障概率的近似值构造为设计变量的显式函数,称为“故障概率函数(FPF)”。 FPF表示为在基于仿真的可靠性分析中获得的样本值的加权和。在每次迭代中进行解耦所需的计算工作只是单次可靠性分析。建立FPF的近似值后,可以将目标RBO问题解耦为确定性问题。同时,将所提出的加权方法与去耦方法和顺序近似优化框架相结合。通过工程实例说明了所提出方法的效率和准确性。

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