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An iterated local search algorithm for the construction of large scale D-optimal experimental designs

Daniel Palhazi Cuervo, Peter Goos () and Kenneth Sörensen

Working Papers from University of Antwerp, Faculty of Business and Economics

Abstract: 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 clearly outperforms the original coordinate-exchange algorithm.

Keywords: Optimal design of experiments; D-optimality criterion; Metaheuristic; Iterated local search; Coordinate-exchange algorithm (search for similar items in EconPapers)
Pages: 22 pages
Date: 2013-04
New Economics Papers: this item is included in nep-cmp and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:ant:wpaper:2013006

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