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.
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Acknowledgements
The authors are grateful to the referees for their valuable and helpful comments and suggestions. The research of Hung is supported by National Science Foundation grants DMS 0905753 and CMMI 0927572. This work was supported in part by National Science Council under grant NSC 99-2118-M-006-006-MY2 (Chen) and NSC 100-2628-M-002-011-MY4 (Wang), the Taida Institute of Mathematical Sciences, Center for Advanced Study in Theoretical Sciences, the National Center for Theoretical Sciences (Taipei Office) and the Mathematics Division of the National Center for Theoretical Sciences (South) in Taiwan.
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Chen, RB., Hsieh, DN., Hung, Y. et al. Optimizing Latin hypercube designs by particle swarm. Stat Comput 23, 663–676 (2013). https://doi.org/10.1007/s11222-012-9363-3
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DOI: https://doi.org/10.1007/s11222-012-9363-3