Numerical and experimental generated data during project https://doi.org/10.1109/ACCESS.2024.3407978

The dataset was generated using a procedure for a fast globalized optimization of passive microwave components. It combines a machine learning procedure, specifically, an iterative construction and refinement of fast surrogates (with infill criterion being a minimization of the predictor-yielded objective improvement) with a response feature technology, where the metamodel targets suitably appointed characteristic points of the circuit outputs. Identification of the infill points is executed using a particle swarm optimization algorithm. Numerical experiments carried out using two microstrip circuits demonstrate the capability for a global search of the proposed algorithm, and its superior performance over direct nature-inspired-based optimization and surrogate-assisted search at the level of complete circuit characteristics.

本数据集基于无源微波组件快速全局优化流程生成。该流程融合机器学习方法与响应特征技术：其中机器学习环节具体为迭代构建并精调快速代理模型（fast surrogates），其填充准则为最小化预测器生成的目标改进量；元模型以电路输出的指定特征点为优化目标。填充点的识别通过粒子群优化算法（Particle Swarm Optimization）实现。采用两款微带电路开展的数值实验验证了所提算法的全局搜索能力，且在完整电路特性层面，该算法的性能优于直接基于自然启发的优化方法与代理辅助搜索策略。