Bio-Inspired Optimization and Machine Learning for Multi-Band Impedance Matching Networks

Abstract

The intelligent design of multi-band impedance matching networks was investigated through the integration of bio-inspired optimization and ma- chine learning classifiers. The Hippopotamus Opti- mization Algorithm (HOA) was employed in conjunc- tion with Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest models to derive accurate and fabrication-ready design param- eters. The optimal configuration, defined by a width of 2.7936 mm, spacing of 0.6103 mm, and length of 1.0893 mm, produced a reflection coefficient (S11) of −29.1456 dB, indicating excellent impedance matching across the target frequency band. Among the classi- fiers, the SVM achieved the highest generalization ac- curacy of 96.76% and the lowest mean squared error of 0.3174, surpassing the performance of ANN and Ran- dom Forest. The developed framework reduces reliance on computationally intensive electromagnetic simula- tions, shortens design time, and maintains high predic- tive precision. These results confirm the effectiveness of combining evolutionary optimization with machine learning for the efficient and compact design of multi- band RF matching networks.