  Reliability-based design optimization (RBDO) provides a rational and sound
framework for finding the optimal design while taking uncertainties into
ac-count. The main issue in implementing RBDO methods, particularly stochastic
simu-lation based ones, is the computational burden arising from the evaluation
of reliability constraints. In this contribution, we propose an efficient
method which ap-proximates the failure probability functions (FPF) to decouple
reliability. Based on the augmentation concept, the approximation of FPF is
equivalent to density estimation of failure design samples. Unlike traditional
density estimation schemes, where the esti-mation is conducted in the entire
design space, in the proposed method we iteratively partition the design space
into several subspaces according to the distribution of fail-ure design
samples. Numerical results of an illustrative example indicate that the
pro-posed method can improve the computational performance considerably.
