A new sampling-based RBDO method via score function with reweighting scheme and application to vehicle designs

Sampling-based methods are general but time consuming for solving a reliability-based design optimization (RBDO) problem. However, one of the major drawbacks of using Monte Carlo simulation (MCS) to compute the sensitivities of reliability functions is computation intensive. In order to alleviate the computation burden, score function together with the MCS method is exploited to compute the stochastic sensitivities of reliability functions. Furthermore by reweighting an initial set of samples, the objective and constraint functions become smooth functions of changes of the parameters of probability distribution, rather than the stochastic functions obtained using MCS. A new sampling-based RBDO method by using score function with reweighting scheme is proposed to perform sensitivities analysis of reliability functions to improve the computational efficiency and accuracy. Several numerical examples are used to show the advantages of the proposed method. Comparisons to the conventional methods are made and discussed. Two large-scale industrial vehicle design problems are solved to demonstrate the feasibility of the proposed method.