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.本数据集通过面向微波无源电路的低成本可靠多目标优化（Multiobjective Optimization, MOO）流程生成。该技术充分利用代理模型的特性，特别是人工神经网络（Artificial Neural Networks, ANNs）与多分辨率电磁（Electromagnetic, EM）分析技术。我们将搜索流程集成至机器学习（Machine Learning, ML）框架内，每一轮迭代均可从当前帕累托集的表征形式中选取多个增补采样点。该采样点集合由多目标进化算法（Multiobjective Evolutionary Algorithm, MOEA）优化人工神经网络元模型得到。当满足收敛条件时流程终止，收敛条件定义为连续迭代所得的非支配解集合之间存在显著相似性。