Reliability-based design optimization (RBDO) is intrinsically a double-loop procedure since it involves an overall optimization and an iterative reliability assessment at each search point. Due to the double-loop procedure, the computational expense of RBDO is normally very high. Current RBDO research is focused on performance functions having explicit analytical expression and readily available gradients. This paper addresses a more challenging type of RBDO problem in which the performance functions are computation intensive. These computation intensive functions are often considered as a “black-box” and their gradients are not available or not reliable. Based on the reliable design space (RDS) concept proposed earlier by the authors, this paper proposes a Reliable Space Pursuing (RSP) approach, in which RDS is first identified and then gradually refined while optimization is performed. It theoretically avoids the nested optimization and probabilistic assessment loop. This approach can apply to RBDO problems with either analytical or blackbox performance functions. Three well known numerical problems from the literature are used to test and demonstrate the effectiveness of RSP.

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