Statistics and Computing

, Volume 26, Issue 1–2, pp 15–28 | Cite as

Optimal design of large-scale screening experiments: a critical look at the coordinate-exchange algorithm

  • Daniel Palhazi Cuervo
  • Peter Goos
  • Kenneth Sörensen
Article

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 outperforms the original coordinate-exchange algorithm.

Keywords

Optimal design of experiments D-optimality criterion  Coordinate-exchange algorithm Metaheuristic Iterated local search 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Daniel Palhazi Cuervo
    • 1
  • Peter Goos
    • 1
    • 2
  • Kenneth Sörensen
    • 1
  1. 1.Faculty of Applied EconomicsUniversity of AntwerpAntwerpBelgium
  2. 2.Faculty of Bioscience EngineeringUniversity of LeuvenLeuvenBelgium

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