Abstract
Electromagnetic optimization utilizing finite element or similar numerical techniques always carries a heavy burden of computation; therefore, an efficient optimizer reducing the number of objective function calls to find the optimum accurately is essential. Kriging as a regression model is able to locate the optimum; meanwhile, it also can produce the response surface of objective function only based on the spatial correlation of limited information. However, the storage of correlation matrices generated by a kriging optimizer, along with the iterative optimization process, is an issue in the context of the electromagnetic optimization problems with many design variables and multiple optimal objectives. The proposed hybrid kriging methodology incorporating a direct algorithm is able to maintain memory occupation of the optimizer as a nearly constant level. It performs efficiently and reliably while coping with the large-scale multivariable optimization tasks. The feasibility and efficiency of the proposed hybrid kriging are verified via a classic analytic function and the multivariable benchmark TEAM Workshop problem 22.
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