Optimum Design of Balanced SAW Filters Using Multi-Objective Differential Evolution

  • Kiyoharu Tagawa
  • Yukinori Sasaki
  • Hiroyuki Nakamura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6457)


Three Multi-Objective Differential Evolutions (MODEs) that differ in their selection schemes are applied to a real-world application, i.e., the multi-objective optimum design of the balanced Surface Acoustic Wave (SAW) filter used in cellular phones. In order to verify the optimality of the Pareto-optimal solutions obtained by the best MODE, those solutions are also compared with the solutions obtained by the weighted sum method. Besides, from the Principal Component Analysis (PCA) of the Pareto-optimal solutions, an obvious relationship between the objective function space and the design parameter space is disclosed.


Surface Acoustic Wave Trial Vector Objective Function Space Design Parameter Space Electric Output Signal 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hashimoto, K.: Surface Acoustic Wave Devices in Telecommunications – Modeling and Simulation. Springer, Heidelberg (2000)CrossRefzbMATHGoogle Scholar
  2. 2.
    Meier, M., Baier, T., Riha, G.: Miniaturization and advanced functionalities of SAW devices. IEEE Trans. on Microwave Theory and Techniques 49(2), 743–748 (2001)CrossRefGoogle Scholar
  3. 3.
    Goto, S., Kawakatsu, T.: Optimization of the SAW filter design by immune algorithm. In: Proc. of IEEE International Ultrasonics, Ferroelectrics, and Frequency Control Joint 50th Anniversary Conference, pp. 600–603 (2004)Google Scholar
  4. 4.
    Meltaus, J., Hamalainen, P., Salomaa, M.M., Plessky, V.P.: Genetic optimization algorithms in the design of coupled SAW filters. In: Proc. of IEEE International Ultrasonics, Ferroelectrics, and Frequency Control Joint 50th Anniversary Conference, pp. 1901–1904 (2004)Google Scholar
  5. 5.
    Tagawa, K., Matsuoka, M.: Optimum design of surface acoustic wave filters based on the Taguchi’s quality engineering with a memetic algorithm. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 292–301. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Tagawa, K., Kojima, N.: Multi-objective optimum design of DMS filters using robust engineering and genetic algorithm. In: Proc. of IEEE CEC, pp. 2208–2214 (2006)Google Scholar
  7. 7.
    Tagawa, K.: Multi-objective optimum design of balanced SAW filters using generalized differential evolution. WSEAS Trans. on System 8(8), 923–932 (2009)Google Scholar
  8. 8.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution – A Practical Approach to Global Optimization. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  9. 9.
    Kukkonen, S., Lampinen, J.: GDE3: The third evolution step of generalized differential evolution. In: Proc. of IEEE CEC, pp. 443–450 (2005)Google Scholar
  10. 10.
    Zitzler, E., et al.: Performance assessment of multiobjective optimizations: an analysis and review. IEEE Trans. on Evolutionary Computation 7(2), 117–132 (2003)CrossRefGoogle Scholar
  11. 11.
    Kojima, T., Suzuki, T.: Fundamental equations of electro-acoustic conversion for an interdigital surface-acoustic-wave transducer by using force factors. Japanese Journal of Applied Physics Supplement 31, 194–197 (1992)CrossRefGoogle Scholar
  12. 12.
    Bockelman, D.E., Eisenstadt, W.R.: Combined differential and common-mode scattering parameters: theory and simulation. IEEE Trans. on Microwave Theory and Techniques 43(7), 1530–1539 (1995)CrossRefGoogle Scholar
  13. 13.
    Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  14. 14.
    Zielinski, K., Laur, R.: Variants of differential evolution for multi-objective optimization. In: Proc. of IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, pp. 91–98 (2007)Google Scholar
  15. 15.
    Pal, S., Das, S., Basak, A.: Design of time-modulated linear arrays with a multi-objective optimization approach. Progress In Electromagnetics Research B 23, 83–107 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kiyoharu Tagawa
    • 1
  • Yukinori Sasaki
    • 2
  • Hiroyuki Nakamura
    • 2
  1. 1.Kinki UniversityHigashi-OsakaJapan
  2. 2.Panasonic Electronic Devices Co., Ltd.OsakaJapan

Personalised recommendations