An efficient, black-box multiobjective optimization technique is presented, which is capable of simultaneously optimizing designs for performance as well as robustness when input tolerance values are not known a priori. During the optimization process, adaptive statistical surrogate mappings between input variables and output objectives are formulated within a model selection framework. These statistical models can be evaluated in fractions of a second and serve as an efficient surrogate for a more computationally intensive process, such as an electromagnetic simulation. By exploiting the speed offered from surrogate modeling techniques, new, high-performance designs can be quickly identified. In addition, complete tolerance analysis can be conducted within the optimization loop, which provides designers with critical information regarding the robustness of designs. To demonstrate the effectiveness of this approach, it will be applied to the optimization of a capacitively loaded monopole and a wideband Vivaldi antenna.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering