Efficient decoupling approach for reliability-based optimization based on augmented Line Sampling and combination algorithm

This paper presents a novel decoupling approach to efficiently solve a class of reliability-based design optimization (RBDO) problems by means of augmented Line Sampling. The proposed approach can fully decouple the original RBDO by replacing the probabilistic constraint with the failure probability function (FPF), which is an explicit function of the design variables. One attractive feature is that the main numerical cost associated with this decoupling comes with only one implementation of augmented Line Sampling, which is actually highly efficient. And for the sake of accuracy, the proposed approach incorporates decoupling with the sequential optimization framework to solve the RBDO problem iteratively. On top of that, an optimal combination algorithm is proposed to reuse the information through aggregating the local estimates of FPF obtained in different iterations to produce an improved estimate, resulting in a more accurate and stable solution. Examples are given to show the effectiveness and efficiency of the proposed approach.