针对高速机动目标协同拦截中作战资源与时空约束难以兼顾的问题，本文提出一种基于改进SA-ACO算法的协同拦截时序优化方法。首先，构建发射调度双层规划模型：外层依据预设拦截概率阈值确定最少发射数量，内层协同优化各拦截弹的最优发射时间窗口。其次，针对内层连续域寻优难题，设计自适应模拟退火-蚁群（SA-ACO）混合算法作为求解器，融合动态混合调控与经验启发策略，克服传统算法易陷局部最优且收敛慢的缺陷。仿真表明，该方法在保证高拦截概率前提下，能快速输出最少弹量与最优时序组合；其收敛速度、寻优精度及稳定性显著提升，为多弹协同调度提供了高效鲁棒的决策方案。

To address the difficulty of balancing the operational resources and spatiotemporal constraints in the cooperative interception against high-speed maneuvering targets，this paper proposes a cooperative interception timing optimization method based on an improved SA-ACO algorithm.Firstly，a bilevel programming model for launch scheduling is constructed：the outer layer determines the minimum number of interceptors based on a preset interception probability threshold，while the inner layer cooperatively optimizes the optimal launch time windows for the interceptor missiles.Secondly，to address the difficulty in the continuous-domain optimization of the inner layer，an adaptive simulated annealing-ant colony optimization （SA-ACO） hybrid algorithm is designed as the solver.The shortcomings of traditional algorithms，which are prone to getting stuck in local optima and have slow convergence are overcome by integrating the dynamic hybrid regulation and experience-inspired strategies.Simulations show that the proposed method quickly outputs the optimal combination of minimum interceptor quantity and launch timing while ensuring a high interception probability.It achieves the significant improvements in convergence speed，optimization accuracy and stability，providing an efficient and robust decision-making scheme for multi-missile cooperative scheduling.