Sampling Design Method of Fast Optimal Latin Hypercube

被引：0
|
作者：
Ye P. [1 ,2 ]
Pan G. [1 ,2 ]
Gao S. [1 ,2 ]
机构：
[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an
[2] Key Laboratory for Unmanned Underwater Vehicle, Northwestern Polytechnical University, Xi'an
来源：
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2019年 / 37卷 / 04期
关键词：
Design of experiments; Latin hypercube design; Optimal sampling design method; Translational propagation algorithm;
D O I：
10.1051/jnwpu/20193740714
中图分类号：
学科分类号：
摘要：
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. © 2019 Journal of Northwestern Polytechnical University.
引用
收藏
页码：714 / 723
页数：9
相关论文
共 9 条
  • [1] Ye P., Pan G., Dong Z., Ensemble of Surrogate Based Global Optimization Methods Using Hierarchical Design Space Reduction, Structural and Multidisciplinary Optimization, 58, 2, pp. 537-554, (2018)
  • [2] Liu H., Ong Y., Cai J., A Survey of Adaptive Sampling for Global Metamodeling in Support of Simulation-Based Complex Engineering Design, Structural and Multidisciplinary Optimization, 57, 1, pp. 393-416, (2017)
  • [3] Liu H., Xu S., Wang X., Sequential Sampling Designs Based on Space Reduction, Engineering Optimization, 47, 7, pp. 867-884, (2015)
  • [4] Dong H., Song B., Dong Z., Et al., Multi-Start Space Reduction(MSSR) Surrogate-Based Global Optimization Method, Structural and Multidisciplinary Optimization, 54, 4, pp. 907-926, (2016)
  • [5] Liu X., Guo B., Multi-Objective Experimentation Design Optimization Based on Modified ESE Algorithms, Systems Engineering and Electronics, 32, 2, pp. 410-414, (2010)
  • [6] Liu X., Chen Y., Jing X., Et al., Optimized Latin Hypercube Sampling Method and Its Application, Journal of National University of Defense Technology, 33, 5, pp. 73-77, (2011)
  • [7] Zhu H., Liu L., Long T., Et al., A Novel Algorithm of Maximin Latin Hypercube Design Using Successive Local Enumeration, Engineering Optimization, 44, 5, pp. 551-564, (2012)
  • [8] Ye K., Li W., Sudjianto A., Algorithmic Construction of Optimal Symmetric Latin Hypercube Designs, Journal of Statistical Planning and Inference, 90, 1, pp. 145-159, (2000)
  • [9] Viana F., Gerhard V., Balabanov V., An Algorithm for Fast Optimal Latin Hypercube Design of Experiments, International Journal for Numerical Methods in Engineering, 82, 2, pp. 135-156, (2010)
1