The development of a hybridized particle swarm for kriging
hyperparameter tuning
The development of a hybridized particle swarm for kriging
hyperparameter tuning
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 lift
kriging, particle swarm optimization, hyperparameter tuning
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Holden, C.M.E.
66fd6373-7d88-48e3-9d86-4a74421db4da
4 January 2011
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Holden, C.M.E.
66fd6373-7d88-48e3-9d86-4a74421db4da
Toal, David J.J., Bressloff, N.W., Keane, A.J. and Holden, C.M.E.
(2011)
The development of a hybridized particle swarm for kriging
hyperparameter tuning.
Engineering Optimization, 43 (6).
(doi:10.1080/0305215X.2010.508524).
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 lift
Text
The_Development_of_a_Hybridized_Particle_Swarm_for_Kriging_Hyperparameter_Tuning.pdf
- Accepted Manuscript
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Published date: 4 January 2011
Keywords:
kriging, particle swarm optimization, hyperparameter tuning
Identifiers
Local EPrints ID: 172477
URI: http://eprints.soton.ac.uk/id/eprint/172477
PURE UUID: 16696778-c4ee-4800-8816-b50501676141
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Date deposited: 27 Jan 2011 08:29
Last modified: 14 Mar 2024 02:53
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Author:
C.M.E. Holden
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