A Strategy for the Design of High-Order RC Active Filters Using Evolutionary Algorithms
DOI:
https://doi.org/10.33414/rtyc.52.1-22.2025Keywords:
evolutionary algorithms, high order filter design, selection of passive components, filter design strategyAbstract
This paper presents a modular design strategy for high-order filters implemented in cascade using evolutionary algorithms (EAs). The proposed method demonstrates notable advantages over those formulated in the literature for sizing this type of filters. Four state-of-the-art EAs are employed: PSO (Particle Swarm Optimization), DE (Differential Evolution), ADE (Average Differential Evolution), and EHO (Elephant Herding Optimization). Two tenth-order Chebyshev filters considered in previous works are adopted as case studies. The results show that the proposed strategy obtains filter configurations with lower design errors than other alternatives, even when using lower-cost components. The computational effort is also notably lower. A statistical comparison among the EAs used shows that DE and ADE are more efficient in terms of the quality of the found configurations.
Downloads
References
Beşkirli, M., & Kiran, M. S. (2023). Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm. Biomimetics, 8(7). https://doi.org/10.3390/biomimetics8070540
Deliyannis, T., Sun, Y., & Fidler, J. (1999). Continuous_Time_Active_Filter_Design CRC 1999.
Demšar, J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 7, 1–30.
Durmuş, B., Temurtaş, H., & Özyön, S. (2020). The design of multiple feedback topology Chebyshev low-pass active filter with average differential evolution algorithm. Neural Computing and Applications, 32(22), 17097–17113. https://doi.org/10.1007/s00521-020-04922-7
García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the CEC’2005 Special Session on Real Parameter Optimization. Journal of Heuristics, 15(6), 617–644. https://doi.org/10.1007/s10732-008-9080-4
Hiçdurmaz, B., Ertaç Durak, F., Özyön, S., Dumlupınar Üniversitesi, K., Fakültesi, M., & Elektronik Mühendisliği, E. (2019). The Estimation of Bessel Type Low-Pass Active Filter Parameters with Charged System Search Algorithm. International Scientific and Vocational Studies Journal, 3(2), 67–75.
Jin, Y. X., Cheng, H. Z., Yan, J. Y., & Zhang, L. (2007). New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electric Power Systems Research, 77(3–4), 227–233. https://doi.org/10.1016/j.epsr.2006.02.016
Karki, J. (2023). Nota de Aplicación: Active Low-Pass Filter Design. www.ti.com
Kuyu, Y. Ç., & Vatansever, F. (2023). Heap-based optimizer embedded with search strategies applied to high-order analog filter designs: a comparative study with up-to-date metaheuristics. Neural Computing and Applications, 35(2), 1447–1467. https://doi.org/10.1007/s00521-022-07835-9
Lampinen, J., & Zelinka, I. (1999). Mixed integer-discrete-continuous optimization by differential evolution. In Proceedings of the 5th International Conference on Soft Computing , 71–76.
Lovay, M., Peretti, G., & Romero, E. (2022). Diseño de filtros activos basados en optimización de cría de elefantes. Jornadas de Ciencia y Tecnología .
Lovay, M., Romero, E., & Peretti, G. (2016). Aplicación del algoritmo de Optimización por Enjambre de Partículas en el dimensionamiento óptimo de componentes para Filtros Activos. Proceedings of SII 2016, 5th Argentine Symposium on Industrial Informatics, 45 JAIIO - 45th Argentine Conference on Informatics, 13–24.
Raut R., & Swamy M. N. S. (2010). Modern Analog Filter Analysis and Design. Wiley.
Şimşir, Ş., & Taşpınar, N. (2021). A novel discrete elephant herding optimization-based PTS scheme to reduce the PAPR of universal filtered multicarrier signal. Engineering Science and Technology, an International Journal, 24(6), 1428–1441. https://doi.org/10.1016/j.jestch.2021.03.001
Slowik, A. (2020). Swarm Intelligence Algorithms. CRC Press.
Temurtaş, H. (2020). The estimation of low and high-pass active filter parameters with opposite charged system search algorithm. Expert Systems with Applications, 155. https://doi.org/10.1016/j.eswa.2020.113474
Yu X., & Gen M. (2010). Introduction to Evolutionary Algorithms. Springer.
Zumbahlen, H. (2007). Nota de Aplicación: Basic linear design, analog devices (Inc.: Norwood, Ed.; Vol. 11).
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mónica Andrea Lovay, Gabriela Marta Peretti, Eduardo Abel Romero

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.