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The development of a hybridized particle swarm for kriging hyperparameter tuning

By David J.J. Toal, N.W. Bressloff, A.J. Keane and C.M.E. Holden

Abstract

Optimizations involving high-fidelity simulations can become prohibitively expensive when an exhaustive search is employed. To remove this expense a surrogate model is often constructed. One of the most popular techniques for the construction of such a surrogate model is that of kriging. However, the construction of a kriging model requires the optimization of a multi-model likelihood function, the cost of which can approach that of the high-fidelity simulations upon which the model is based. The article describes the development of a hybridized particle swarm algorithm which aims to reduce the cost of this likelihood optimization by drawing on an efficient adjoint of the likelihood. This hybridized tuning strategy is compared to a number of other strategies with respect to the inverse design of an airfoil as well as the optimization of an airfoil for minimum drag at a fixed lif

Topics: QC, T1
Year: 2011
OAI identifier: oai:eprints.soton.ac.uk:172477
Provided by: e-Prints Soton

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  1. namely the 15 and 25 variable problems. Based on the top ten performing strategies for both of these cases, the data for which can be found in Toal (2009), the hybrid strategy deļ¬ned in Table 3 was selected.

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