Computational Intelligence-Based Methodology for Antenna Development

The antenna design is a challenging task, which might be time-consuming using conventional computational methods that typically require high computational capability, due to the need for several sweeps and re-running processes. This work proposes an efficient and accurate computational intelligence-...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.1860-1870
Main Authors: Melo, Marcello Caldano De, Santos, Pedro Buarque, Faustino, Everaldo, Bastos-Filho, Carmelo J. A., Cerqueira Sodre, Arismar
Format: Article
Language:English
Subjects:
Antenna design
Antennas
Antennas design
Artificial intelligence
Artificial neural networks
Back propagation
Back propagation networks
computational intelligence
Computational modeling
Design optimization
Dipole antennas
Evolutionary algorithms
Genetic algorithms
Heuristic methods
Impedance matching
machine learning
Mathematical models
Methodology
multi-objective optimization
Multilayer perceptrons
Multiple objective analysis
Optimization
Reflector antennas
Sorting algorithms
Training
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Summary:The antenna design is a challenging task, which might be time-consuming using conventional computational methods that typically require high computational capability, due to the need for several sweeps and re-running processes. This work proposes an efficient and accurate computational intelligence-based methodology for the antenna design and optimization. The computational technical solution consists of a surrogate model application, composed of a Multilayer Perceptron (MLP) artificial neural network with backpropagation for the regression process. Combined with the surrogate model, two multiobjective optimization meta-heuristic strategies, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), are used to overcome the mentioned issues from the traditional antenna design method. A study of case considering a dipole antenna for the 3.5 GHz 5G band is reported, as proof of the proposed methodology concept. Comparisons of antenna impedance matching obtained by the proposed methodology, numerical full-wave results from ANSYS HFSS and experimental result from the antenna prototype are performed for demonstrating its applicability and effectiveness for antenna development.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3137198