为了减少随机抽样的次数并保证蒙特卡罗法的数值模拟精度，对比引入了重要抽样法和拉丁超立方体抽样方法；

To reduce sampling number and assure simulation precision, Importance Sampling method and Latin Hypercube Sampling method are coupled with Neumann expansion SFEM respectively.

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通过不同规模和不同优化准则的拉丁超立方体最优实验设计，验证改进算法的应用效果。

The application results of the improved algorithm are verified by searching Latin hypercube optimal design of varying scales under different optimization criteria.

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