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Tytuł artykułu

Hybrid optimization algorithm using evolutionary algorithm and local search method with its application in digital FIR filter design

Autorzy
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Warianty tytułu
Konferencja
Evolutionary Computation and Global Optimization 2008 / National Conference (11 ; 2-4.06.2008 ; Szymbark, Poland)
Języki publikacji
EN
Abstrakty
EN
This paper is devoted to the application of an Evolutionary Algorithm to the design of Finite Impulse Response filters (FIR). A hybrid algorithm is proposed, which consists of a robust global optimization method (Evolutionary Algorithm - EA) and a good local optimization method (Quasi-Newton - QN). An experimental comparison of the hybrid algorithm against EA and QN alone indicates that EA yields filters with the better amplitude characteristics than QN. Furthermore the hybrid method yields filters with even better amplitude characteristics and in some times, it needs significantly less time than EA alone to reach good solutions.
Rocznik
Tom
Strony
23--32
Opis fizyczny
Bibliogr. 16 poz., tab., wykr.
Twórcy
autor
Bibliografia
  • [1] Andreas Antoniou. Digital Filters: Analysis, Design, and Applications. McGraw-Hill, Inc, 2nd edition, 1993.
  • [2] T. Baeck, D.B. Fogel and Z. Michalewicz, editors. "Handbook of Evolutionary Computation". Institute of Physics Publishing, Ltd, Bristol, UK, 1997.
  • [3] C.G. Broyden. The convergence of a class of double-rank minimization algorithms, Part I. IMA J. Appl. Math., 6:76-90, 1970.
  • [4] William C. Davidon. Variable metric method for minimization. SIAM Journal on Optimization, 1(1):1-17, 1991.
  • [5] R. Fletcher. A new approach to variable metric algorithms. Comput. J., 13:317-322, 1970.
  • [6] Gaspare Galati, editor. Advanced Radar Techniques and Systems. Institution of Engineering and Technology, 1993.
  • [7] D. Goldfarb. A family of variable metric methods derived by variational means. Math. Comp., 24:23-26, 1970.
  • [8] William E. Hart. Adaptive Global Optimization with Local Search. PhD thesis, San Diego, CA, 1994.
  • [9] M.R. Hestenes and E. Stiefel. Methods of conjugate gradients for solving linear systems. JResNatBurStand, 49:409-436, 1952.
  • [10] Abdul J. Jerri. The Gibbs phenomenon in Fourier analysis, splines and wavelet approximations., volume 446 of Mathematics and its Applications. Kluwer Academic Publishers, Dordrecht, 1998.
  • [11] M. Land. Evolutionary algorithms with local search lor combinatorial optimization, 1998.
  • [12] Ko-Hsin Liang, Xin Yao, and Charles Newton. Combining landscape approximation and local search in global optimization. In Peter J. Angeline, Zbyszek Michalewicz, Marc Schoenauer, Xin Yao, and Ali Zalzala, editors, Proceedings of the Congress on Evolutionary Computation, volume 2, pages 1514-1520, Mayflower Hotel, Washington D.C., USA, 6-9 1999. IEEE Press.
  • [13] Zbigniew Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag, 1996.
  • [14] John G. Proakis and Dimitris G. Manolakis. Digital Signal Processing: Principles. Algorithms, and Applications. Prentice Hall, Upper Saddle River, NJ, 3rd edition, 1996.
  • [15] D.F. Shanno. Conditioning of quasi-Newton methods for function minimization. Math. Comp., 24:647-657, 1970.
  • [16] M.I. Skolnik. Introduction to Radar Systems. 1962.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA9-0035-0002
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