Discrete particle swarm optimization for constructing uniform design on irregular regions
Discrete particle swarm optimization for constructing uniform design on irregular regions
复制标题
DOI：
10.1016/j.csda.2013.10.015
复制
发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
Ray‐Bing Chen;Yen-Wen Hsu;Ying Hung;Weichung Wang
中科院分区：
文献类型：
--
作者：
Ray‐Bing Chen;Yen-Wen Hsu;Ying Hung;Weichung Wang
中心复合偏差（CCD）被用来度量设计在不规则试验区域上的均匀性。然而，如何有效地计算基于CCD的最优均匀设计仍然是一个挑战。针对这一问题，我们提出了一种基于粒子群优化的算法，有效地找到最优的均匀设计相对于CCD标准。采用基于图形处理器（GPU）的并行计算技术来加速计算。几个二维到五维的基准问题被用来说明所提出的算法的优点。通过解决数据中心热管理中的真实的应用，我们进一步证明了所提出的算法可以扩展到包含所需的空间填充属性，例如非塌陷属性。
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.