An efficient first-order scheme for reliability based optimization of stochastic systems

This contribution presents a novel approach for solving reliability-based optimization (RBO) problems. In particular, the reliability-based optimization of structural systems under stochastic loading is considered. The approach is based on a descent-feasible direction and a line search strategy, allowing to explore the space of the design variables most efficiently. The proposed method is monotonically convergent, that is, it generates a sequence of steadily improved feasible designs. An efficient simulation technique is used for the purpose of evaluating the reliability constraints. On the other hand, the sensitivity of the reliability constraints with respect to the design variables is estimated by an approach based on the local behavior of the performance functions that define the failure domains. Numerical results show that only a moderate number of reliability estimates has to be performed during the design process. A numerical example showing the efficiency and effectiveness of the approach reported herein is presented.