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.