Active set strategy-based sequential approximate programming for reliability-based design optimization

To improve the evaluation efficiency of failure probability in RBDO models with uncertainty, many RIA-based, PMA-based methods have evolved as a powerful procedure, including the modified reliability index approach (MRIA), PMA two-level, PMA with sequential approximate programming (SAP). However, MRIA may encounter inefficiency and instability when applied to complex concave performance functions, and so does PMA two-level, not for PMA with SAP. The active set strategy-based SAP (ASS-based SAP) for PMA is proposed to accelerate computational efficiency through establishing an active set strategy and a deciding factor. The active set strategy defined by using inequality is to identify the feasible most probable target point (MPTP) in the inner loop. The decision factor integrates the reliability index and the active set strategy to quickly renew the active constraints in the outer loop. The reliability assessment and outer optimization are driven simultaneously, thereby the computational efficiency is strengthened. Numerical examples are compared with other reliability methods to demonstrate the excellent performance of the proposed method in efficiency and robustness. Results also show that the proposed method has the ability to solve complex RBDO problems.

Automated summary

In this paper, a SAP method based on active set strategy is proposed to improve the evaluation efficiency of failure probability in RBDO models. By establishing an active set strategy and a decision factor, the reliability assessment and outer optimization are carried out simultaneously, thus accelerating the computation process. Numerical examples demonstrate the excellent performance and capability of the proposed method in efficiency and robustness for solving complex RBDO problems.