Reliability-based design optimization (RBDO) offers a powerful tool to deal with the structural design with heterogeneous interval parameters concurrently. However, it is time-consuming in the practical engineering design. Therefore, a novel sequential moving asymptote method (SMAM) is proposed to improve the computational efficiency for convex model in this study, in which the nested double-loop optimization problem is decoupled to a sequence of deterministic suboptimization problems based on the method of moving asymptotes. In addition, the sensitivity of reliability index is derived, so the finite difference for the nested optimization loop can be avoided to tremendously improve the computational efficiency. Then, the accuracy of the SMAM is proved based on the error analysis. Furthermore, the Kreisselmeier-Steinhauser (KS) function is used to assemble the multiple constraints to deal with the parallel and series RBDO problems. One benchmark mathematical example, three numerical examples, and one complex civil engineering example, i.e., tower crane, are tested to demonstrate the efficiency of the proposed method by comparison with other existing methods, and the results indicate that SMAM offers a general and effective tool for non-probabilistic reliability analysis and optimization.

基于可靠性的设计优化（RBDO）提供了强大的工具来同时处理具有不同间隔参数的结构设计。但是，这在实际工程设计中非常耗时。因此，本研究提出了一种新颖的顺序移动渐近线方法（SMAM），以提高凸模型的计算效率，该方法将嵌套双环优化问题解耦到基于移动渐近线方法的确定性次优化问题序列。此外，推导了可靠性指标的敏感性，因此可以避免嵌套优化循环的有限差异，从而极大地提高了计算效率。然后，通过误差分析证明了SMAM的准确性。此外，Kreisselmeier-Steinhauser（KS）函数用于组合多个约束以处理并行和串联RBDO问题。通过与其他现有方法进行比较，测试了一个基准数学示例，三个数值示例以及一个复杂的土木工程示例（塔式起重机），以证明该方法的有效性，结果表明SMAM提供了一种通用有效的工具用于非概率可靠性分析和优化。