The authors present a new approach for evolutionary antenna design. Through exploiting efficient adjoint sensitivity analysis techniques, the antenna structure evolves to better satisfy the design constraints. The coordinates of a selected number of control vertices are chosen as optimisation parameters thus enabling evolution to arbitrary shapes. The authors approach is illustrated through the design of a number of microstrip structures.

Abstract-We propose an optimization methodology suitable for the design of various antenna structures. This methodology includes a rapidly-converging iterative scheme. In each iteration stage, the algorithm generates a parameterized Cauchy model using the available results from previous iterations. Optimization is then applied to this Cauchy model to obtain better design parameters that are also used in enhancing the accuracy of the model. This cycle continues until the specifications are met. In addition, this on-the-fly technique produces an analytical model of the behavior of the antenna structure. Sensitivity and tolerance analysis can thus be efficiently carried out without the need for further costly electromagnetic simulations.

We present a novel space mapping-based approach for the optimization of high frequency structures. A Cauchy rational function approximation with variable data tolerances is first constructed using the available inexpensive coarse model simulations. The parameters of the rational function are obtained through a fast linear program problem that is guaranteed to reach the global solution. The fine model data that become available during optimization are then added to the data set and the tolerances of the domain are adjusted to improve the overall model accuracy. The resulting new surrogate model is then utilized in optimization to predict a better design. Our approach is illustrated through two examples.

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