Optimizing latin hypercube design for sequential sampling of computer experiments

F. Xiong, Y. Xiong, W. Chen*, S. Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Space-filling and projective properties are desired features in the design of computer experiments to create global metamodels to replace expensive computer simulations in engineering design. The goal in this article is to develop an efficient and effective sequential Quasi-LHD (Latin Hypercube design) sampling method to maintain and balance the two aforementioned properties. The sequential sampling is formulated as an optimization problem, with the objective being the Maximin Distance, a space-filling criterion, and the constraints based on a set of pre-specified minimum one-dimensional distances to achieve the approximate one-dimensional projective property. Through comparative studies on sampling property and metamodel accuracy, the new approach is shown to outperform other sequential sampling methods for global metamodelling and is comparable to the one-stage sampling method while providing more flexibility in a sequential metamodelling procedure.

Original languageEnglish (US)
Pages (from-to)793-810
Number of pages18
JournalEngineering Optimization
Issue number8
StatePublished - Aug 2009


  • Global metamodelling
  • Latin Hypercube design
  • Optimal design
  • Sequential sampling
  • Space-filling

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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