A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs

Ricardo García-Ródenas, José Carlos García-García, Jesús López-Fidalgo, José Ángel Martín-Baos, Weng Kee Wong

研究成果: Article同行評審

17 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號106844
期刊Computational Statistics and Data Analysis
144
DOIs
出版狀態Published - 2020 4月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 計算數學
  • 計算機理論與數學
  • 應用數學

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