In this paper, we propose a hybrid optimization approach that combines the Efficient Global Optimization (EGO) algorithm with Taguchi's method. This hybrid optimized algorithm is suited for problems with expensive cost functions. As a Bayesian analysis optimization algorithm, EGO algorithm begins with fitting the Kriging model with n sample points, and finds the (n+1)th point where the expected improvement is maximized to update the model. We employ Taguchi's method in EGO to obtain the (n+1)th point in this paper. A numerical simulation demonstrates that our algorithm has advantage over the original EGO. Finally, we apply this hybrid optimized algorithm to optimize an ultra-wide band (UWB) transverse electromagnetic (TEM) horn antenna and a linear antenna array. Compared to Taguchi's method and the Integer Coded Differential Evolution Strategy, our algorithm converges to the global optimal value more efficiently.
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