Numerical and experimental generated data during project https://doi.org/10.1109/TMTT.2024.3359703

The dataset was generated using a procedure for low-cost and reliable multiobjective optimization (MOO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multiresolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points selected from the present representation of the Pareto set. This collection is formed by optimizing the ANN metamodel using a multiobjective evolutionary algorithm (MOEA). The procedure concludes upon convergence, defined as a significant similarity between the sets of nondominated solutions acquired through consecutive iterations.