A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs
Keywords: 
Approximate design
Efficiency
Equivalence theorem
Information matrix
Metaheuristics
Optimality criteria
Issue Date: 
2020
Publisher: 
Elsevier
Project: 
info:eu-repo/grantAgreement/MECD/Formación de profesorado universitario- FPU 2016/FPU16%2F00792/ES/FPU16%2F00792
ISSN: 
1872-7352
Citation: 
García-Ródenas, R. (Ricardo); García-García, J.C. (José Carlos); López-Fidalgo, J. (Jesús); et al. "A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs". Computational Statistics & Data Analysis. 144, 2020, 106844
Abstract
Several common general purpose optimization algorithms are compared for finding A- and D-optimal designs for different types of statistical models of varying complexity, including high dimensional models with five and more factors. The algorithms of interest include exact methods, such as the interior point method, the Nelder–Mead method, the active set method, the sequential quadratic programming, and metaheuristic algorithms, such as particle swarm optimization, simulated annealing and genetic algorithms. Several simulations are performed, which provide general recommendations on the utility and performance of each method, including hybridized versions of metaheuristic algorithms for finding optimal experimental designs. A key result is that general-purpose optimization algorithms, both exact methods and metaheuristic algorithms, perform well for finding optimal approximate experimental designs.

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