Reliability-based design optimization using a family of methods of moving asymptotes

Abstract

In this study, an effective method for reliability-based design optimization is proposed enhancing sequential optimization and reliability assessment (SORA) method by a family of methods of moving asymptotes (MMA) approximations. In SORA, reliability estimation and deterministic optimization are performed sequentially. And the sensitivity and function value of probabilistic constraint at the most probable point (MPP) are obtained in the process of finding reliability information. In this study, a family of MMA approximations are constructed by utilizing the sensitivity and function value of the probabilistic constraint at the MPP. So, no additional evaluation of the probabilistic constraint is required in constructing MMA approximations. Moreover, no additional evaluation of the probabilistic constraint is required in the deterministic optimization of SORA by using a family of MMA approximations. The efficiency and accuracy of the proposed method were verified through numerical examples.