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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30007
Title: | 利用粒子群演算法與圖形處理器尋找最佳拉丁超立方設計 Optimizing Latin Hypercube Designs by Particle Swarm with GPU Acceleration |
Authors: | Dai-Ni Hsieh 謝岱霓 |
Advisor: | 王偉仲 |
Keyword: | 拉丁超立方設計,粒子群演算法,圖形處理器, Latin hypercube design (LHD),particle swarm optimization (PSO),graphic processing unit (GPU), |
Publication Year : | 2011 |
Degree: | 碩士 |
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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30007 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 數學系 |
Files in This Item:
File | Size | Format | |
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ntu-100-1.pdf Restricted Access | 938.96 kB | Adobe PDF |
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