为提高可靠性设计优化（RBDO）效率，本文通过将过渡马尔科夫链蒙特卡洛（TMCMC）和代理抽样概率密度函数（SS-PDF）引入可靠性优化设计的双层框架中，建立了一种可靠性优化两阶段求解的高效方法。所提方法首先通过贝叶斯模型框架将复杂约束优化问题分解为可行设计空间探索与最优解集搜索两阶段，并采用TMCMC方法实现了从可行域到最优解集的全局搜索。其次，通过引入SS-PDF将优化过程中不同设计参数情况下的可靠性约束求解问题转换为参数不确定性下的可靠性分析问题，以共用样本的形式实现以少的功能函数调用量对不同设计参数对应的可靠性约束的高效求解。借助于RBDO双层分析框架的鲁棒性、基于TMCMC的贝叶斯全局优化的高效性以及不同设计参数处可靠性约束的解耦，所提方法不仅具有广泛的适用范围，而且能够显著提高可靠性优化的效率。将建立的可靠性优化设计方法应用至数值算例和航空发动机涡轮轴可靠性优化设计模型，验证了所提方法的可行性和高效性。

To enhance the efficiency of Reliability-Based Design Optimization (RBDO), this paper introduces an efficient two?stage solution method for reliability optimization by integrating Transitional Markov Chain Monte Carlo (TMCMC) and a surrogate sampling probability density function (SS?PDF) into a two?level RBDO framework. The proposed approach first decomposes the complex constrained optimization problem into two stages—exploration of the feasible design space and search for the optimal solution set—within a Bayesian model framework, and employs TMCMC to achieve a global search from the feasible region to the optimal solution set. Secondly, by incorporating the SS?PDF, the reliability constraint evaluation under different design parameters is transformed into a reliability analysis under parametric uncertainty, enabling efficient evaluation of reliability constraints corresponding to various design parameters with minimal function calls through shared samples. Leveraging the robustness of the two?level RBDO analytical framework, the efficiency of Bayesian global optimization based on TMCMC, and the decoupling of reliability constraints at different design parameters, the proposed method not only exhibits broad applicability but also significantly improves the efficiency of reliability optimization. Finally, the established reliability based design optimization method is applied to a numerical example and a reliability optimization model of an aero engine turbine shaft, and the results demonstrate the feasibility and high efficiency of the proposed approach.