In literature, heuristic algorithms have been successfully applied to a number of electromagnetic problems. The associated cost functions are commonly linked to full-wave analysis, leading to complexity and high computational expense. Artificial Neural Network is one of the most effective biological inspired techniques. In this article, an efficient surrogate model is trained to replace the full-wave analysis in optimizing the bandwidth of microstrip antenna. The numerical comparison between ANN substitution model and full-wave characterization shows significant improvements in time convergence and computational cost. To verify the robustness of this approach, all these concepts are integrated into a case study represented by a rectangular ring antenna with proximity-coupled feed antenna.
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