Optimizing Latin Hypercube Designs by Particle Swarm with GPU Acceleration
Date Issued
2011
Date
2011
Author(s)
Hsieh, Dai-Ni
Abstract
Due to the expensive cost of many computer and physical experiments, it is important to carefully choose a small number of experimental points uniformly spreading out the experimental domain in order to obtain most information from these few runs. Although space-filling Latin hypercube designs (LHDs) are popu- lar ones that meet the need, LHDs need to be optimized to have the space-filling property. As the number of design points or variables becomes large, the to- tal number of LHDs grows exponentially. The huge number of feasible points makes this a difficult discrete optimization problem. In order to search the opti- mal LHDs efficiently, we propose a population based algorithm which is adapted from the standard particle swarm optimization (PSO) and customized for LHD. Moreover, we accelerate the adapted PSO for LHD (LaPSO) via graphic process- ing unit (GPU). According to the examined cases, the proposed LaPSO is more stable compared to other two methods and capable of improving some known results.
Subjects
Latin hypercube design (LHD)
particle swarm optimization (PSO)
graphic processing unit (GPU)
Type
thesis
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