Maximizing the load current of a ferromagnetic frequency doubler using a genetic algorithm
DOI:
https://doi.org/10.15407/fmmit2024.39.135Keywords:
parametric optimization, genetic algorithm, steady-state, nonlinear mathematical models, frequency doubler, ferromagnetic frequency doubler, optimization algorithms.Abstract
This paper proposes the optimization of the load current of a ferromagnetic frequency doubler using a
genetic algorithm (GA). The existing mathematical model is considered and its Python equivalent is
developed. The developed model was compared with the original mathematical model. The existing
Python libraries for GA are briefly reviewed. The pygad library is used for optimization and the given
parameters are considered. The main optimization criterion is to maximize the value of the load current.
Four rounds of optimizations were performed. The first three rounds of optimization required only a
change in the input voltage within different limits, which does not require an actual change in the device.
The fourth round of optimization, in turn, expanded the third round by additionally introducing changes
in the parameters of the magnetic branches, which would require a physical change in the device. The
use of GA made it possible to quickly optimize and adjust the optimizations based on the obtained values.
The results of the optimizations showed a significant increase in the load current of the ferromagnetic
frequency doubler compared to the baseline parameters before the optimization
References
Johnson D. G. & Wemore J. M. (2021). Technology and Society, second edition: Building Our Sociotechnical Future (Inside Technology). The MIT Press. pp. 600.
Ahmad A. & Camp C. V. (2024). Advanced Optimization Applications in Engineering. IGI Global. pp. https://doi.org/10.4018/979-8-3693-2161-4
Mukherjee G., Mallik B. B., Kar. R. & Chaudhary A. (2024). Advances on Mathematical Modeling and Optimization with Its Applications (Emerging Technologies). CRC Press. pp. 261.
Ghadertootoonchi A., Solaimanian A., Davoudi M. & Aghtaie M. (2024). Energy System Modeling and Optimization: A Practical Guide Using Pyomo. Springer. pp. 194. https://doi.org/10.1007/978-3-031-65906-5_3
Kouba M., Ammar M., Dhouib D. & Mnejia S. (2024). Optimization in the Agri-Food Supply Chain: Recent Studies. Wiley-ISTE. pp. 288. https://doi.org/10.1002/9781394316977
Ahmad A. & Camp C. (2024). Advanced Optimization Applications in Engineering. IGI Global. pp. 300. https://doi.org/10.4018/979-8-3693-2161-4
Ponce-Ortega J., Ochoa-Barragan R. & Ramirez-Marquez C. (2024). Optimization of Chemical Processes: A Sustainable Perspective. Springer. pp. 730. https://doi.org/10.1007/978-3-031-57270-8
Zhou Y. and Chen W. (2022). Analysis and Optimization of Low-Voltage and High-Current Matrix Current-Doubler Rectifiers Integrated Magnetic Components. Applied Mathematics, Modeling and Computer Simulation. vol. 30. pp. 240-247.
Rivadeneira D., Villegas M., Procel L. M. & Trojman L. (2020). Optimization of Active Voltage Rectifier / Doubler Designed in 90 nm Technology. 2020 IEEE 11th Latin American Symposium on Circuits & Systems (LASCAS), San Jose, Costa Rica, pp. 1-4.
Liu X., Zhang Y., Wu C., Wang H., Wang B., Xu Y., Xiao F., Zhou J. & Jin Z. (2022). A 220 GHz High Efficiency Doubler Based on Function-Based Harmonic Impedance Optimization Method. Journal of Infrared, Millimeter, and Terahertz Waves. vol. 43. https://doi.org/10.1007/s10762-022-00842-w
Samotyj V. (2016). Nelinijni Matematychni Modeli Elementiv System Keruvannia [Nonlinear mathematical models of control system elements]. Spolom. pp. 274.
Rajasekar S. (2024). Numerical Methods. CRC Press. pp. 558 https://doi.org/10.1201/9781032649931
