Abstract
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 article 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 found in previous literature. 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. Supplementary materials for this article are available online.
| Original language | English |
|---|---|
| Pages (from-to) | 43-49 |
| Number of pages | 7 |
| Journal | Technometrics |
| Volume | 58 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2016 Jan 2 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
- Applied Mathematics
Cite this
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Optimizing two-level supersaturated designs using swarm intelligence techniques. / Phoa, Frederick Kin Hing; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee.
In: Technometrics, Vol. 58, No. 1, 02.01.2016, p. 43-49.Research output: Contribution to journal › Article
TY - JOUR
T1 - Optimizing two-level supersaturated designs using swarm intelligence techniques
AU - Phoa, Frederick Kin Hing
AU - Chen, Ray-Bing
AU - Wang, Weichung
AU - Wong, Weng Kee
PY - 2016/1/2
Y1 - 2016/1/2
N2 - 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 article 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 found in previous literature. 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. Supplementary materials for this article are available online.
AB - 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 article 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 found in previous literature. 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. Supplementary materials for this article are available online.
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U2 - 10.1080/00401706.2014.981346
DO - 10.1080/00401706.2014.981346
M3 - Article
AN - SCOPUS:84975707266
VL - 58
SP - 43
EP - 49
JO - Technometrics
JF - Technometrics
SN - 0040-1706
IS - 1
ER -