Optimizing Latin hypercube designs by particle swarm

Ray Bing Chen, Dai Ni Hsieh, Ying Hung, Weichung Wang

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


Latin hypercube designs (LHDs) are widely used in many applications. As the number of design points or factors becomes large, the total number of LHDs grows exponentially. The large number of feasible designs makes the search for optimal LHDs a difficult discrete optimization problem. To tackle this problem, we propose a new population-based algorithm named LaPSO that is adapted from the standard particle swarm optimization (PSO) and customized for LHD. Moreover, we accelerate LaPSO via a graphic processing unit (GPU). According to extensive comparisons, the proposed LaPSO is more stable than existing approaches and is capable of improving known results.

Original languageEnglish (US)
Pages (from-to)663-676
Number of pages14
JournalStatistics and Computing
Issue number5
StatePublished - Sep 2013

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics


  • Graphic processing unit (GPU)
  • Latin hypercube design
  • Particle swarm optimization


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