Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.
Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.
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DOI：
10.1080/00401706.2014.981346
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Wong WK
中科院分区：
文献类型：
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作者：
Phoa FK;Chen RB;Wang W;Wong WK
超饱和设计（SSD）通常用于减少具有大量因子的筛选实验中的实验运行次数。随着研究中使用的因子越来越多，由于因子水平设置的大量可行选择，寻找最佳SSD变得越来越具有挑战性。本文通过一种基于群体智能的算法来解决这一离散优化问题。使用常用的E（s2）标准作为说明性的例子，我们提出了一种算法来找到E（s2）-最优SSD，通过显示它们达到和的理论下限。我们表明，我们的算法始终产生SSD，至少是有效的，从传统的CP交换方法的计算工作量，频率找到E（s2）-最优SSD，也有很好的潜力，找到D3-，D4-和D5-最优SSD。
Supersaturated designs (SSDs) are often used to reduce the number of experimental runs in screening experiments with a large number of factors. As more factors are used in the study, the search for an optimal SSD becomes increasingly challenging because of the large number of feasible selection of factor level settings. This paper tackles this discrete optimization problem via an algorithm based on swarm intelligence. Using the commonly used E(s2) criterion as an illustrative example, we propose an algorithm to find E(s2)–optimal SSDs by showing that they attain the theoretical lower bounds in and. We show that our algorithm consistently produces SSDs that are at least as efficient as those from the traditional CP exchange method in terms of computational effort, frequency of finding the E(s2)-optimal SSD and also has good potential for finding D3–, D4– and D5–optimal SSDs.