Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Possibilistic Framework for Multi-objective Optimization Under Uncertainty Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

18. Ant Colony Optimization for Optimal Low-Pass Filter Sizing

verfasst von : Loubna Kritele, Bachir Benhala, Izeddine Zorkani

Erschienen in: Bioinspired Heuristics for Optimization

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Automatically Generating Assessment Tests Within Higher Education Context Thanks to Genetic Approach
Nächstes Kapitel Maximum a Posteriori Based Evolutionary Algorithm
Literatur
1.
Grimbleby, J. B. (2000). Automatic analogue circuit synthesis using genetic algorithms. IEE Proceedings - Circuits, Devices and Systems, 147 (6).
2.
Aarts, E., & Lenstra, K. (2003). Local search in combinatorial optimization. Princeton: Princeton University Press.MATH
3.
Benhala, B. (2014). Ant colony optimization for optimal low-pass butterworth filter design. WSEAS Transactions on Circuits and Systems, 13, 313–318.
4.
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.
5.
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).
6.
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).
7.
Dorigo, M., DiCaro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life Journal, 5, 137–172.CrossRef
8.
Dorigo, M., & Krzysztof, S. (2006). An introduction to ant colony optimization. In Approximation algorithms and metaheuristics.
9.
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.
10.
Elzeki, O., Reshad, M., & Elsoud, M. (2012). Improved max-min algorithm in cloud computing. International Journal of Computer Applications, 5, 22–27.
11.
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).
12.
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.
13.
Glover, F. (1990). Tabu search-part ii. ORSA Journal on Computing, 2, 4–32.
14.
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.
15.
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.
16.
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.
17.
Neumann, F., Sudholt, D., Witt., C. (2009). Analysis of different mmas aco algorithms on unimodal functions and plateaus. Swarm Intelligence, 3, 35–68.
18.
Paarman, L. D. (2007). Design and analysis of analog filters. Norwell, MA: Kluwer.
19.
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).
20.
Schaumann, R., & Valkenburg, M. V. (2001). Design of analog filters. New York: Oxford University Press.
21.
Sttzle, T., & Hoos, H. (2000). Max-min ant system. Future Generation Computer System, 16, 889–914.CrossRef
22.
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.
23.
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).
24.
Yu, B., Yang, Z., & Yao, B. (2009). An improved ant colony optimization for vehicle routing problem. European Journal of Operational Research, 196, 171–176.CrossRef
Metadaten
Titel
Ant Colony Optimization for Optimal Low-Pass Filter Sizing
verfasst von
Loubna Kritele
Bachir Benhala
Izeddine Zorkani
Copyright-Jahr
2019
Buch
Bioinspired Heuristics for Optimization

Print ISBN: 978-3-319-95103-4

Electronic ISBN: 978-3-319-95104-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-95104-1_18