A hybrid optimization approach for automated parameter estimation problems | Zendy

Argáez M. | Zendy; Klie H. | Zendy; Quintero C. | Zendy; Velázquez L. | Zendy; Wheeler M. | Zendy

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A hybrid optimization approach for automated parameter estimation problems

Author(s) -

Argáez M.,

Klie H.,

Quintero C.,

Velázquez L.,

Wheeler M.

Publication year - 2007

Publication title -

pamm

Language(s) - English

Resource type - Journals

ISSN - 1617-7061

DOI - 10.1002/pamm.200700948

Subject(s) - simultaneous perturbation stochastic approximation , mathematical optimization , surrogate model , computer science , estimation theory , stochastic optimization , coupling (piping) , optimization problem , algorithm , mathematics , stochastic process , engineering , mechanical engineering , statistics

We present a hybrid optimization approach for solving automated parameter estimation models. The hybrid approach is based on the coupling of the Simultaneous Perturbation Stochastic Approximation (SPSA) [1] and a Newton‐Krylov Interior‐Point method (NKIP) [2] via a surrogate model. The global method SPSA performs a stochastic search to find target regions with low function values. Next, we generate a surrogate model based on the points of regions on which the local method NKIP algorithm is applied for finding an optimal solution. We illustrate the behavior of the hybrid optimization algorithm on one testcase. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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