中文摘要：

针对有效全局优化（EGO）方法计算时间长、收敛速度慢且易陷入局部最优区域的缺点，提出一种基于克里金模型的多采样点序列全局优化算法．在序列优化过程中，该算法主要引入中点距离最小舍弃方法来获取多个采样点，并以广义EGO方法作为填充采样准则，对多个采样点进行并行优化，以提高算法效率，同时有效平衡局部和全局的搜索行为．两个数值测试算例和一个工程仿真实例验证了该方法的有效性和实用性．

英文摘要：

A Kriging-based sequence global optimization method for multiple sampling points was put forward to improve the Kriging-based EGO （efficient global optimization） method. The Kriging-based EGO method has longer calculating time, slower convergence speed, and it is easy to fall into local op- timal region. The proposed algorithm mainly introduces in the deleting minimum middle distance cri- terion to obtain multiple sampling, and uses the generalized EGO method as infill sampling criteria to concurrently optimize the multiple sampling points. The method enhances computational efficiency. Meanwhile, it can also effectively balance global and local search direction. Two numerical tests and an engineering simulation example show tha（ the proposed method is efficient and applicable.