Optimizing Latin hypercube designs by particle swarm

Authors:
Ray-Bing Chen;Dai-Ni Hsieh;Ying Hung;Weichung Wang
Affiliations:
Department of Statistics, National Cheng Kung University, Tainan, Taiwan 701;Institute of Statistical Science, Academia Sinica, Taipei, Taiwan 115;Department of Statistics and Biostatistics, Rutgers University, Piscataway, USA 08854;Department of Mathematics, National Taiwan University, Taipei, Taiwan 106
Venue:
Statistics and Computing
Year:
2013

Citing 4
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Abstract

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.