论文标题
基于群智能优化的两级多层阶乘实验的设计选择
Design Selection for Two-Level Multi-Stratum Factorial Experiments Based on Swarm Intelligence Optimization
论文作者
论文摘要
对于非结构化的实验单元，弗里斯（Fries）和亨特（Hunter，1980）引起的最小畸变是选择常规分数阶乘设计的流行标准。随后，许多相关研究集中在多层次阶乘设计上，其中多个错误术语来自实验单元的复杂结构。 Chang and Cheng（2018）提出了一个以贝叶斯风格的畸变标准，用于选择多层阶乘设计，可以将其视为Fries and Hunter（1980）中的广义版本。但是，他们没有提出用于搜索最小像差设计的算法。粒子群优化（PSO）算法是一种流行的优化方法，已在各种应用中广泛使用。在本文中，我们提出了一个新版本的PSO，以选择常规以及非规则的多层设计。为了选择普通的单词，我们将定义单词视为PSO中的粒子，然后将PSO与设计键矩阵链接。对于非规则的多层设计，我们将治疗组合视为PSO中的颗粒。提供了几个数字插图。
For unstructured experimental units, the minimum aberration due to Fries and Hunter (1980) is a popular criterion for choosing regular fractional factorial designs. Following which, many related studies have focused on multi-stratum factorial designs, in which multiple error terms arise from the complicated structures of experimental units. Chang and Cheng (2018) proposed a Bayesian-inspired aberration criterion for selecting multi-stratum factorial designs, which can be considered as a generalized version of that in Fries and Hunter (1980). However, they did not propose algorithms for searching for minimum aberration designs. The particle swarm optimization (PSO) algorithm is a popular optimization method that has been widely used in various applications. In this paper, we propose a new version of the PSO to select regular as well as nonregular multi-stratum designs. To select regular ones, we treat defining words as particles in the PSO and link the PSO with design key matrices. For nonregular multi-stratum designs, we treat treatment combinations as particles in the PSO. Several numerical illustrations are provided.
