针对大型运载火箭加筋圆柱壳轻量化设计面临的混合整数变量多耦合强、计算成本高、优化收敛性差等问题，提出了一种基于探索/开发竞争并行采样的序列近似优化方法（SAOCPS）。该方法建立加筋圆柱壳轴压承载能力的增广径向基函数近似模型以提高后屈曲分析效率，并基于双精英种群进化的开发采样策略挖掘潜在最优区域，提升算法局部寻优性能。根据离散-连续变量低维投影特点，确定探索样本新增水平，采用元素交换-概率跃迁相结合的排列优化方法进行均匀性改进，实现探索样本对离散-连续设计空间的均匀覆盖，提升算法全局寻优性能。利用性能增益驱动的探索/开发竞争采样机制，平衡算法对设计空间的探索/开发力度，引导高维高耗时混合整数优化过程快速收敛。在大直径大载荷加筋圆柱壳轻量化设计应用中，本文方法在初始样本数量大幅缩减90%以上时，仍获得了与文献工作相当的优化结果，且比经典PoF优化方法进一步减重了279.8 kg，验证了本文方法的有效性和工程适用性。

A sequential approximate optimization method based on exploitation/exploration competing parallel sampling strategy (SAOCPS) is proposed to conquer the difficulties of coupling multi-mixed-integer variables, high computational cost, and poor optimization convergence in the lightweight design of stiffened cylindrical shells for large launch vehicles. In this method, the augmented radial basis function (ARBF) approximate model of axial load-carrying capacity is constructed to improve the efficiency of post-buckling analysis for the stiffened cylindrical shells. A dual-elite population evolution-based exploitation strategy is developed to exploit the information in promising regions, thereby enhancing the local optimization performance of SAOCPS. Meanwhile, the new levels are determined according to the one-dimensional projection characteristics of discrete and continuous variables. The combination of element exchange and probabilistic transition is employed to enhance the performance of the permutation optimization method, which makes the uniform coverage of the exploration sample points to the discrete-continuous design space. It improves the global optimization performance of SAOCPS. Ultimately, the exploration/exploitation competitive mechanism driven by performance gains is introduced for the balance between exploration and exploitation, thereby guiding the rapid convergence of the high-dimensional and time-consuming mixed integer optimization problems. In applying the lightweight design of large-diameter stiffened cylindrical shells, the optimization results of SAOCPS are still comparable to those of the literature work even if the initial sample points are greatly reduced by more than 90%. Furthermore, the weight is reduced by 279.8 kg compared with the classical PoF optimization method. The effectiveness and superiority of the proposed method are validated for the prospect of the engineering applications.