一种快速优化拉丁超立方试验设计方法

Sampling Design Method of Fast Optimal Latin Hypercube

工程设计优化中,优化试验设计方法常常用于求解大型复杂系统问题。针对传统优化试验设计方法计算耗时长、效率低的问题,提出一种快速优化拉丁超立方试验设计方法:在拉丁超立方抽样框架下,采用基于最大最小距离准则连续局部枚举方法设计生成高性能小尺寸基础样本,然后利用平移传播算法通过"平移"基础样本快速获得大尺寸试验样本。结合提出的样本尺寸调整策略,使该方法可以快速得到空间填充性能和映射性能良好的任意尺寸试验样本。测试结果表明快速优化拉丁超立方试验设计方法能够兼顾设计效率和样本性能,优于已有典型试验设计方法。

In engineering design optimization, the optimal sampling design method is usually used to solve large-scale and complex system problems. A sampling design(FOLHD) method of fast optimal Latin hypercube is proposed in order to overcome the time-consuming and poor efficiency of the traditional optimal sampling design methods. FOLHD algorithm is based on the inspiration that a near optimal large-scale Latin hypercube design can be established by a small-scale initial sample generated by using Successive Local Enumeration method and Translational Propagation algorithm. Moreover, a sampling resizing strategy is presented to generate samples with arbitrary size and owing good space-filling and projective properties. Comparing with the several existing sampling design methods, FOLHD is much more efficient in terms of the computation efficiency and sampling properties.