S-BORM: Reliability-based optimization of general systems using buffered optimization and reliability method
提出一种高效算法S-BORM，用于解决一般系统（含大量割集）的可靠性优化问题，通过缓冲失效概率和近端束方法迭代求解，数值示例验证了其高精度和计算效率。
Reliability-based optimization (RBO) is crucial for identifying optimal risk-informed decisions for designing and operating engineering systems. However, its computation remains challenging as it requires a concurrent task of optimization and reliability analysis. Moreover, computation becomes even more complicated when considering performance of a general system, whose failure event is represented as a link-set of cut-sets. This is because even when component events have smooth and convex limit-state functions, the system limit-state function has neither property, except in trivial cases. To address the challenge, this study develops an efficient algorithm to solve RBO problems of general system events. We employ the buffered optimization and reliability method (BORM), which utilizes, instead of the conventional failure probability definition, the buffered failure probability. The proposed algorithm solves a sequence of difference-of-convex RBO models iteratively by employing a proximal bundle method. For demonstration, we design various numerical examples with increasing complexity that include up to 10,062 cut-sets, which are solved by the proposed algorithm within a reasonable computational time with high accuracy. We also demonstrate the algorithm’s robustness by performing extensive parametric studies.