The development of a hybridized particle swarm for kriging hyperparameter tuning
| Autor: | Carren M. E. Holden, Neil W. Bressloff, Andy J. Keane, David J. J. Toal |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Hyperparameter
Mathematical optimization Control and Optimization Lift (data mining) Applied Mathematics Particle swarm optimization Brute-force search Management Science and Operations Research Industrial and Manufacturing Engineering Computer Science Applications Surrogate model Kriging Multi-swarm optimization Likelihood function Mathematics |
| Zdroj: | Engineering Optimization. 43:675-699 |
| ISSN: | 1029-0273 0305-215X |
| DOI: | 10.1080/0305215x.2010.508524 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|