Abstract
In this paper, a recently proposed global heuristic search optimization technique, namely, Modified Firefly Algorithm (MFFA) is considered for the design of the 8th order infinite impulse response (IIR) low pass (LP) digital filter. This modified version of FFA is considered to achieve quality output response by means of properly tuned control parameters over conventional Firefly Algorithm (FFA). Newly defined randomization parameter and modification in updating formula in MFFA makes it a perfect search tool in multidimensional search space. With this approach better exploration and exploitation are achieved, which have resulted in faster convergence to near global optimal solution. The performance of the proposed MFFA based approach is compared to the performances of some well accepted evolutionary algorithms such as particle swarm optimization (PSO) and real coded genetic algorithm (RGA). From the simulation study it is established that the proposed optimization technique MFFA outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and the solution quality, i.e., the stop band attenuation, transition width, pass band and stop band ripples.
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Keywords
- Particle Swarm Optimization
- Filter Design
- Firefly Algorithm
- Infinite Impulse Response
- Gravitational Search Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Oppenheim, A.V., Schafer, R.W., Buck, J.R.: Discrete-Time Signal Processing. Prentice-Hall, NJ (1999)
Hussain, Z.M., Sadik, A.Z., O’Shea, P.: Digital Signal Processing- An Introduction with MATLAB Applications. Springer (2011)
Xue, L., Rongchun, Z., Qing, W.: Optimizing the design of IIR filter via genetic algorithm. In: Proc. IEEE Int. Conf. on Neural Networks and Signal Processing, vol. 1, pp. 476–479 (2003)
Karaboga, N., Cetinkaya, B.: Design of minimum phase digital IIR filters by using genetic algorithm. In: Proc. IEEE 6th Nordic Signal Processing Symposium, Finland, pp. 29–32 (2004)
Dai, C., Chen, W., Zhu, Y.: Seeker optimization algorithm for digital IIR filter design. IEEE Trans. on Industrial Electronics 57(5), 1710–1718 (2010)
Ahmad, S.U., Andreas, A.: Cascade-form multiplier less FIR filter design using orthogonal genetic algorithm. In: IEEE Int. Symp. on Signal Processing and Info. Tech., pp. 932–937 (2006)
Tsai, J.T., Chou, J.H., Liu, T.K.: Optimal design of digital IIR filters by using hybrid Taguchi genetic algorithm. IEEE Trans. on Industrial Electronics 53(3), 867–879 (2006)
Karaboga, D., Horrocks, D.H., Karaboga, N., Kalinli, A.: Designing digital FIR filters using Tabu search algorithm. In: IEEE Int. Symp. on Circuits and Systems, vol. 4, pp. 2236–2239 (1997)
Chen, S.: IIR Model Identification using Batch-Recursive Adaptive Simulated Annealing Algorithm. In: Proc. 6th Annual Chinese Auto. and Comp. Sc. Conf., pp. 151–155 (2000)
Karaboga, N.: A New Design Method Based on Artificial Bee Colony Algorithm for Digital IIR Filters. Journal of the Franklin Institute 346(4), 328–348 (2009)
Karaboga, N., Cetinkaya, B.: Design of Digital FIR Filters using Differential Evolution Algorithm. Circuits Systems Signal Processing 25(5), 649–660 (2006)
Panda, G., Pradhan, P.M., Majhi, B.: IIR System Identification Using Cat Swarm Optimization. Expert Systems with Applications 38(10), 12671–12683 (2011)
Kalinli, A., Karaboga, N.: Artificial Immune Algorithm for IIR Filter Design. Engineering Applications of Artificial Intelligence 18(8), 919–929 (2005)
Najjarzadeh, M., Ayatollahi, A.: FIR Digital Filters Design: Particle Swarm Optimization Utilizing LMS and Minimax Strategies. In: IEEE Int. Symp. on Signal Processing and Information Technology, pp. 129–132 (2008)
Krusienski, D.J., Jenkins, W.K.: A Modified Particle Swarm Optimization Algorithm for Adaptive Filtering. In: IEEE Int. Symp. on Circuits and Systems, pp. 137–140 (2006)
Das, S., Konar, A.: A Swarm Intelligence Approach to the Synthesis of Two-Dimensional IIR Filters. Engineering Applications of Artificial Intelligence 20(8), 1086–1096 (2007)
Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.P.: Gravitation Search Algorithm: Application to the Optimal IIR Filter Design. Journal of King Saud University - Engineering Sciences (2012), doi: http://dx.doi.org/10.1016/j.jksues.2012.12.003
Saha, S.K., Mukherjee, S., Mandal, D., Kar, R., Ghoshal, S.P.: Gravitational search algorithm in digital FIR low pass filter design. In: Third IEEE Int. Conf. on Emerging Applications of Information Technology (EAIT), pp. 52–55 (2012)
Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.P., Mukherjee, V.: A New Design Method Using Opposition-Based BAT Algorithm for IIR System Identification Problem. Int. J. Bio-Inspired Computation 5(2), 99–132 (2013)
Yang, X.S.: Multi-objective firefly algorithm for continuous optimization. Engineering with Computers 29(2), 175–184 (2013)
Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithms with chaos. Communications in Nonlinear Science and Numerical Simulation 18(1), 89–98 (2013)
Fister, I., Fister Jr., I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation (2013), http://dx.doi.org/10.1016/j.swevo.2013.6.1
Gandomi, A.H., Yang, X.S., Alavi, A.H.: Mixed variable structural optimization using firefly algorithm. Computers and Structures 89(23-24), 2325–2336 (2011)
Yang, X.S.: Review of meta-heuristics and generalised evolutionary walk algorithm. International Journal of Bio-inspired Computation 3(2), 77–84 (2011)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimization. International Journal of Bio-inspired Computation 2(2), 78–84 (2010)
Shafaati, M., Mojallali, H.: Modified Firefly Optimization for IIR System Identification. Control Engineering and Applied Informatics 14(4), 59–69 (2012)
Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.P.: IIR Filter Design with Craziness Based Particle Swarm Optimization Technique. World Academy of Science, Engineering and Technology 60, 1628–1635 (2011)
Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.P.: Optimal IIR filter Design Using Novel Particle Swarm Optimization Technique. Int. Journal of Circuits, Systems and Signal Processing 6(2), 151–162 (2012)
Yang, X.-S.: Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)
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Saha, S.K., Kar, R., Mandal, D., Ghoshal, S. (2013). Optimal Stable IIR Low Pass Filter Design Using Modified Firefly Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_10
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DOI: https://doi.org/10.1007/978-3-319-03753-0_10
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