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Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.

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 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 Bulutoglu and Cheng (2004) and Bulutoglu (2007). 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.

SUBMITTER: Phoa FK

PROVIDER: S-EPMC4835032 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.

Phoa Frederick Kin Hing FK Chen Ray-Bing RB Wang Weichung W Wong Weng Kee WK

Technometrics : a journal of statistics for the physical, chemical, and engineering sciences 20160122 1

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 illustr ...[more]

PMID: 27103752

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