Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.
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 usedE(s2) criterion as an illustrative example, we propose an algorithm to findE(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 theE(s2)-optimal SSD and also has good potential for findingD3-,D4- andD5-optimal SSDs.
Institute of Statistical Science, Academia Sinica.
Frederick Kin Hing
FK
Phoa
Department of Statistics, National Cheng Kung University.
Ray-Bing
RB
Chen
Institute of Applied Mathematical Sciences, National Taiwan University.
Weichung
W
Wang
Department of Biostatistics, University of California at Los Angeles.
Weng Kee
WK
Wong
Balanced design; Columnwise-pairwise algorithm; Df-criterion; E(s2)-criterion