TY - JOUR
T1 - Discrete particle swarm optimization for constructing uniform design on irregular regions
AU - Chen, Ray Bing
AU - Hsu, Yen Wen
AU - Hung, Ying
AU - Wang, Weichung
N1 - Funding Information:
The authors are grateful to the reviewers for their comments and suggestions. The research of Hung was supported by National Science Foundation grants DMS 0905753 and CMMI 0927572 . This specific work was supported in part by the 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, the 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.
PY - 2014/4
Y1 - 2014/4
N2 - Central composite discrepancy (CCD) has been proposed to measure the uniformity of a design over irregular experimental region. However, how CCD-based optimal uniform designs can be efficiently computed remains a challenge. Focusing on this issues, we proposed a particle swarm optimization-based algorithm to efficiently find optimal uniform designs with respect to the CCD criterion. Parallel computation techniques based on state-of-the-art graphic processing unit (GPU) are employed to accelerate the computations. Several two- to five-dimensional benchmark problems are used to illustrate the advantages of the proposed algorithms. By solving a real application in data center thermal management, we further demonstrate that the proposed algorithm can be extended to incorporate desirable space-filling properties, such as the non-collapsing property.
AB - Central composite discrepancy (CCD) has been proposed to measure the uniformity of a design over irregular experimental region. However, how CCD-based optimal uniform designs can be efficiently computed remains a challenge. Focusing on this issues, we proposed a particle swarm optimization-based algorithm to efficiently find optimal uniform designs with respect to the CCD criterion. Parallel computation techniques based on state-of-the-art graphic processing unit (GPU) are employed to accelerate the computations. Several two- to five-dimensional benchmark problems are used to illustrate the advantages of the proposed algorithms. By solving a real application in data center thermal management, we further demonstrate that the proposed algorithm can be extended to incorporate desirable space-filling properties, such as the non-collapsing property.
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U2 - 10.1016/j.csda.2013.10.015
DO - 10.1016/j.csda.2013.10.015
M3 - Article
AN - SCOPUS:84890565621
SN - 0167-9473
VL - 72
SP - 282
EP - 297
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -