  Reliability-based design optimization (RBDO) is an active field of research
with an ever increasing number of contributions. Numerous methods have been
proposed for the solution of RBDO, a complex problem that combines optimization
and reliability analysis. Classical approaches are based on approximation
methods and have been classified in review papers. In this paper, we first
review classical approaches based on approximation methods such as FORM, and
also more recent methods that rely upon surrogate modelling and Monte Carlo
simulation. We then propose a general framework for the solution of RBDO
problems that includes three independent blocks, namely adaptive surrogate
modelling, reliability analysis and optimization. These blocks are
non-intrusive with respect to each other and can be plugged independently in
the framework. After a discussion on numerical considerations that require
attention for the framework to yield robust solutions to various types of
problems, the latter is applied to three examples (using two analytical
functions and a finite element model). Kriging and support vector machines
together with their own active learning schemes are considered in the surrogate
model block. In terms of reliability analysis, the proposed framework is
illustrated using both crude Monte Carlo and subset simulation. Finally, the
covariance-matrix adaptation - evolution scheme (CMA-ES), a global search
algorithm, or sequential quadratic programming (SQP), a local gradient-based
method, are used in the optimization block. The comparison of the results to
benchmark studies show the effectiveness and efficiency of the proposed
framework.
