Abstract
In analog filter design, discrete components values such as resistors (R) and capacitors (C) are selected from the series following constant values chosen. Exhaustive search on all possible combinations for an optimized design is not feasible. In this chapter, we present an application of the Ant Colony Optimization (ACO) technique for optimal filter design considering different manufacturing series for both the resistors and capacitors. Three variants of the Ant Colony Optimization are applied, namely, the AS (Ant System), the MMAS (Min-Max AS) and the ACS (Ant Colony System), for the optimal sizing of the Low-Pass Butterworth filter. Different optimal designs of the filter are provided depending on the preference between two conflicting factors, namely the cutoff frequency and selectivity factor. SPICE simulations are used to validate the obtained results/performances. A comparison with published works is also highlighted.
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Grimbleby, J. B. (2000). Automatic analogue circuit synthesis using genetic algorithms. IEE Proceedings - Circuits, Devices and Systems, 147 (6).
Aarts, E., & Lenstra, K. (2003). Local search in combinatorial optimization. Princeton: Princeton University Press.
Benhala, B. (2014). Ant colony optimization for optimal low-pass butterworth filter design. WSEAS Transactions on Circuits and Systems, 13, 313–318.
Benhala, B., Ahaitouf, A., Kotti, M., Fakhfakh, M., Benlahbib, B., Mecheqrane, A., et al. (2011). Application of the ACO technique to the optimization of analog circuit performances. Analog circuits: Applications, design and performance. NOVA Science Publishers.
Benhala, B., Ahaitouf, A., Mechaqrane, A., & Benlahbib, B. (2012). Multiobjective optimization of second generation current conveyors by the ACO technique. In The International Conference on Multimedia Computing and Systems (ICMCS’12) (pp. 1147–1151).
Benhala, B., Ahaitouf, A., Mechaqrane, A., Benlahbib, B., Abarkan, F. A. E., & Fakhfakh, M. (2011). Sizing of current conveyors by means of an ant colony optimization technique. In The IEEE International Conference on Multimedia Computing and Systems (ICMCS’11) (pp. 899–904).
Dorigo, M., DiCaro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life Journal, 5, 137–172.
Dorigo, M., & Krzysztof, S. (2006). An introduction to ant colony optimization. In Approximation algorithms and metaheuristics.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, 26, 29–42.
Elzeki, O., Reshad, M., & Elsoud, M. (2012). Improved max-min algorithm in cloud computing. International Journal of Computer Applications, 5, 22–27.
Fakhfakh, M., Boughariou, M., Sallem, A., & Loulou, M. (2012). Design of low noise amplifiers through flow-graphs and their optimization by the simulated annealing technique. In Advances in monolithic microwave integrated circuits for wireless systems: Modeling and design technologies. IGI Global (pp. 69–88).
Fakhfakh, M., Cooren, Y., Sallem, A., Loulou, M., & Siarry, P. (2010). Analog circuit design optimization through the particle swarm optimization technique. Journal of Analog Integrated Circuits & Signal Processing Springer, 63 N 1, 71–82.
Glover, F. (1990). Tabu search-part ii. ORSA Journal on Computing, 2, 4–32.
Gmez, J. F., Khodr, H. M., Oliveira, P. M. D., Ocque, L., Yusta, J. M., Villasana, R., et al. Ant colony system algorithm for the planning of primary distribution circuits.
Jinhui, Y., Xiaohu, S., Maurizio, M., & Yanchun, L. (2009). An ant colony optimization method for generalized tsp problem. Progress in Natural Science, e18, 1417–1422.
Kotti, M., Benhala, B., Fakhfakh, M., Ahaitouf, A., Benlahbib, B., Loulou, M., et al. (2011). Comparison between pso and aco techniques for analog circuit performance optimization. In E.E.A.o.O.T. IEEE TN CEDAs (Ed.), The International Conference on Microelectronics.
Neumann, F., Sudholt, D., Witt., C. (2009). Analysis of different mmas aco algorithms on unimodal functions and plateaus. Swarm Intelligence, 3, 35–68.
Paarman, L. D. (2007). Design and analysis of analog filters. Norwell, MA: Kluwer.
Sallem, A., Benhala, B., Kotti, M., Fakhfakh, M., Ahaitouf, A., & Loulou, M. (2013). Application of swarm intelligence techniques to the design of analog circuits: Evaluation and comparison. In Springer (Ed.), Analog integrated circuits and signal processing (Vol. 75, pp. 499–516).
Schaumann, R., & Valkenburg, M. V. (2001). Design of analog filters. New York: Oxford University Press.
Sttzle, T., & Hoos, H. (2000). Max-min ant system. Future Generation Computer System, 16, 889–914.
Vural, R., Yildirim, T., Kadioglu, T., & Basargan, A. (2012). Performance evaluation of evolutionary algorithms for optimal filter design. IEEE Transactions on Evolutionary Computation, 16, 135–147.
Vural, R. A., Yildirim, T. (2010). Component value selection for analog active filter using particle swarm optimization. In 2nd ICCAE (Vol. 1, pp. 25–28).
Yu, B., Yang, Z., & Yao, B. (2009). An improved ant colony optimization for vehicle routing problem. European Journal of Operational Research, 196, 171–176.
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Kritele, L., Benhala, B., Zorkani, I. (2019). Ant Colony Optimization for Optimal Low-Pass Filter Sizing. In: Talbi, EG., Nakib, A. (eds) Bioinspired Heuristics for Optimization. Studies in Computational Intelligence, vol 774. Springer, Cham. https://doi.org/10.1007/978-3-319-95104-1_18
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DOI: https://doi.org/10.1007/978-3-319-95104-1_18
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