Çok Amaçlı Atomik Yörünge Arama Algoritması ile Metasezgisel FIR Filtre Tasarımı

Abstract

Bu çalışmada, MATLAB ortamında yüksek performanslı ve düşük dereceli FIR filtreleri tasarlamak için yeni önerilen metasezgisel algoritmalardan biri olan atomik yörünge araması kullanılmıştır. Tasarlanan filtrenin performans parametreleri verilen literatür ile karşılaştırılmıştır. Filtre tasarımının amaç fonksiyonu, geçiş bandı dalgalanmalarının ve durdurma bandı dalgalanmalarının minimizasyonunu, durdurma bandı kenar frekans zayıflamasını, tahmini frekans yanıtı ile ideal filtre yanıtı arasındaki hataların karesel toplamını içerir. Karşılaştırma sonucu, önerilen yöntemin çoğu algoritmadan daha iyi performans gösterdiğini ve pratik uygulamalarda kullanılabileceğini göstermektedir.

Keywords

• Metaheuristic
• Optimization
• FIR Filter Design
• Multi Objective

Metaheuristic FIR Filter Design with Multi-Objective Atomic Orbital Search Algorithm

Abstract

In this study, atomic orbital search, one of the newly proposed metaheuristic algorithms, is used to design high-performance and low-order FIR filters in MATLAB. The performance parameters of the designed filter were compared with the given literature. The objective function of filter design includes minimization of pass band ripples and stopband ripples, stop band edge frequency attenuation, square sum of the errors between the estimated frequency response and the ideal filter response. The comparison result shows the proposed method performs better than most algorithms and can be used in practical applications.

Keywords

• Metaheuristic
• Optimization
• FIR Filter Design
• Multi Objective.

References

1. Azizi, M. (2021a). Atomic orbital search: A novel metaheuristic algorithm. Applied Mathematical Modelling, 93, 657–683. https://doi.org/https://doi.org/10.1016/j.apm.2020.12.021
2. Azizi, M. (2021b). Atomic orbital search: A novel metaheuristic algorithm. Applied Mathematical Modelling, 93, 657–683. https://doi.org/10.1016/j.apm.2020.12.021
3. Bose, D., Biswas, S., Vasilakos, A. V. & Laha, S. (2014). Optimal filter design using an improved artificial bee colony algorithm. Information Sciences, 281, 443–461. https://doi.org/10.1016/j.ins.2014.05.033
4. Chen, S. & Luk, B. L. (2010). Digital IIR filter design using particle swarm optimisation. International Journal of Modelling, Identification and Control, 9(4), 327–335.
5. Gupta, L. & Mehra, R. (2011). Modified PSO based Adaptive IIR Filter Design for System Identification on FPGA. International Journal of Computer Applications, 22(5), 1–7. https://doi.org/10.5120/2583-3569
6. Karaboga, N. (2005). Digital IIR filter design using differential evolution algorithm. Eurasip Journal on Applied Signal Processing, 2005(8), 1269–1276. https://doi.org/10.1155/ASP.2005.1269
7. Karaboga, N. (2009). A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute, 346(4), 328–348. https://doi.org/10.1016/j.jfranklin.2008.11.003
8. Karaboga, N. & Cetinkaya, B. (2004). Design of minimum phase digital IIR filters by using genetic algorithm. Report - Helsinki University of Technology, Signal Processing Laboratory, 46, 29–32.

9. Karaboga, N. & Cetinkaya, B. (2006). Design of digital FIR filters using differential evolution algorithm. Circuits, Systems, and Signal Processing, 25(5), 649–660. https://doi.org/10.1007/s00034-005-0721-7
10. Karaboǧa, N. & Çetinkaya, M. B. (2011). A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm. Turkish Journal of Electrical Engineering and Computer Sciences, 19(1), 175–190. https://doi.org/10.3906/elk-0912-344
11. Karakas, M. F. & Latifoglu, F. (2021). Optimizasyon Tabanlı FIR Süzgeç Tasarımlarında Performans Analizi. European Journal of Science and Technology, 31(31), 8–22. https://doi.org/10.31590/ejosat.958748
12. Karakaş, M. F. & Latifoğlu, F. (2020). Finite Impulse Response Filter Design Using Squirrel Search Algorithm. 2020 Medical Technologies Congress (TIPTEKNO), 1–4.
13. Kaya, T. & İnce, M. C. (2011). Genetik Algoritma Yardımıyla Elde Edilen Yüksek Performa nslı Pencere Fonksiyonlarının Yinelemesiz Sayısal Filtre Tasarımında Kullanımı. May, 16–18.
14. Kumar, A., Subhojit, D. & Londhe, N. D. (2017). Low-Power FIR Filter Design Using Hybrid Artificial Bee Colony Algorithm with Experimental Validation Over FPGA. Circuits, Systems, and Signal Processing, 36(1), 156–180. https://doi.org/10.1007/s00034-016-0297-4
15. Latifoǧlu, F. (2013). A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application. Computer Methods and Programs in Biomedicine, 111(3), 561–569. https://doi.org/10.1016/j.cmpb.2013.05.009
16. Litwin, L. (2000). FIR and IIR digital filters. IEEE Potentials, 19(4), 28–31.
17. Manuel, M. & Elias, E. (2012). Design of Sharp 2D Multiplier-Less Circularly Symmetric FIR Filter Using Harmony Search Algorithm and Frequency Transformation. Journal of Signal and Information Processing, 03(03), 344–351. https://doi.org/10.4236/jsip.2012.33044
18. Najjarzadeh, M. & Ayatollahi, A. (2008). A comparison between genetic algorithm and PSO for linear phase fir digital filter design. International Conference on Signal Processing Proceedings, ICSP, 2134–2137. https://doi.org/10.1109/ICOSP.2008.4697568
19. Oppenheim, A. V. (1999). Discrete-time signal processing. Pearson Education India.
20. Parks, T. W. & Burrus, C. S. (1987). Digital filter design. Wiley-Interscience.
21. Proakis, J. G. (2001). Digital signal processing: principles algorithms and applications. Pearson Education India.
22. Reddy, K. S. & Sahoo, S. K. (2015). An approach for FIR filter coefficient optimization using differential evolution algorithm. AEU - International Journal of Electronics and Communications, 69(1), 101–108. https://doi.org/10.1016/j.aeue.2014.07.019
23. Saha, S. K., Kar, R., Mandal, D. & Ghoshal, S. P. (2014). Harmony search algorithm for infinite impulse response system identification. Computers and Electrical Engineering, 40(4), 1265–1285. https://doi.org/10.1016/j.compeleceng.2013.12.016
24. Sarangi, S. K., Panda, R. & Abraham, A. (2020). Design of optimal low-pass filter by a new Levy swallow swarm algorithm. Soft Computing, 24(23), 18113–18128. https://doi.org/10.1007/s00500-020-05065-6
25. Zhang, G., Gu, Y., Hu, L. & Jin, W. (2003). A novel genetic algorithm and its application to digital filter design. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2, 1600–1605. https://doi.org/10.1109/ITSC.2003.1252754