An Efficient Quantile-Based Adaptive Sampling RBDO with Shifting Constraint Strategy

Abstract

There is an increasing demand for the performance optimization under the reliability constraints in various engineering problems. These problems are commonly known as reliability-based design optimization (RBDO) problems. Among different RBDO frameworks, the decoupled methods are widely accepted for their high efficiency and stability. However, when facing problems with high nonlinearity and nonnormally distributed random variables, they lose their computational performance. In this study, a new efficient decoupled method with two level quantile-based sampling strategy is presented. The strategies introduced for two level sampling followed by information reuse of nearby designs are intended to enhance the sampling from failure region, thus reducing the number of samples to improve the efficiency of sampling-based methods. Compared with the existing methods which decouples RBDO in the design space and thus need to struggle with searching for most probable point (MPP), the proposed method decouples RBDO in the probability space to further make beneficial use of an efficient optimal shifting value search strategy to reach an optimal design in less iterations. By comparing the proposed method with crude MCS and other sampling-based methods through benchmark examples, our proposed method proved to be competitive in dramatically saving the computational cost.