Statistics and Computing
Author:
Keywords:
Optimal design of experiments, D-optimality criterion, Coordinate-exchange algorithm, Metaheuristic, Iterated local search, Science & Technology, Technology, Physical Sciences, Computer Science, Theory & Methods, Statistics & Probability, Computer Science, Mathematics, GENETIC ALGORITHMS, SPLIT-PLOT, CONSTRUCTION, OPTIMIZATION, 0104 Statistics, 0802 Computation Theory and Mathematics, 4901 Applied mathematics, 4903 Numerical and computational mathematics, 4905 Statistics
Abstract:
© 2014, Springer Science+Business Media New York. We focus on the D-optimal design of screening experiments involving main-effects regression models, especially with large numbers of factors and observations. We propose a new selection strategy for the coordinate-exchange algorithm based on an orthogonality measure of the design. Computational experiments show that this strategy finds better designs within an execution time that is 30 % shorter than other strategies. We also provide strong evidence that the use of the prediction variance as a selection strategy does not provide any added value in comparison to simpler selection strategies. Additionally, we propose a new iterated local search algorithm for the construction of D-optimal experimental designs. This new algorithm outperforms the original coordinate-exchange algorithm.