A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm

The uni-modal error surfaces and intrinsic stable behaviors of adaptive finite impulse response (FIR) filters make gradient based algorithms very effective in the design of these filters. Gradient based design methods are well developed for the design of adaptive FIR filters and widely applied to the distinct areas such as noise cancellation, system identification and channel equalization. However, the studies on adaptive infinite impulse response (IIR) filters are not as common as adaptive FIR filters since the stability during the adaptation process may not be ensured in some applications, and the convergence to the optimal design is not always guaranteed due to their multi-modal error surface structures. Gradient based design approaches may often get stuck at a local minimum in a multi-modal error surface and the stability of the designed filter can not be ensured. However, global optimization algorithms based approaches are able to converge to the global minimum in a multi-modal error surface and ensure the stability of the adaptive IIR filter. One of the most recently proposed swarm intelligence based global optimization algorithms is the artificial bee colony algorithm, which simulates the intelligent foraging behavior of honeybee swarms. In this work, a novel approach based on artificial bee colony algorithm is introduced for the design of adaptive FIR and adaptive IIR filters. Simulations are realized for the noise cancellation problem and the performance of the proposed approach is compared to that of some known gradient and evolutionary based approaches.

A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm

The uni-modal error surfaces and intrinsic stable behaviors of adaptive finite impulse response (FIR) filters make gradient based algorithms very effective in the design of these filters. Gradient based design methods are well developed for the design of adaptive FIR filters and widely applied to the distinct areas such as noise cancellation, system identification and channel equalization. However, the studies on adaptive infinite impulse response (IIR) filters are not as common as adaptive FIR filters since the stability during the adaptation process may not be ensured in some applications, and the convergence to the optimal design is not always guaranteed due to their multi-modal error surface structures. Gradient based design approaches may often get stuck at a local minimum in a multi-modal error surface and the stability of the designed filter can not be ensured. However, global optimization algorithms based approaches are able to converge to the global minimum in a multi-modal error surface and ensure the stability of the adaptive IIR filter. One of the most recently proposed swarm intelligence based global optimization algorithms is the artificial bee colony algorithm, which simulates the intelligent foraging behavior of honeybee swarms. In this work, a novel approach based on artificial bee colony algorithm is introduced for the design of adaptive FIR and adaptive IIR filters. Simulations are realized for the noise cancellation problem and the performance of the proposed approach is compared to that of some known gradient and evolutionary based approaches.

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  • Paulo S. R. Diniz, Adaptive Filtering Algorithms and Practical Implementations, Springer, USA, 2008.
  • S. Haykin, Adaptive Filter Theory, Prentice Hall, USA, 2002.
  • D. J. Krusienski, W. K. Jenkins, Design and performance of adaptive systems based on structured stochastic optimization strategies, IEEE Circuits Systems Magazine 5 (2005) 8-20.
  • S. C. Ng, S. H. Leung, C. Y. Chung, A. Luk, W. H. Lau, The genetic search approach: A new learning algorithm for adaptive IIR Şltering, IEEE Signal Processing Magazine 13 (1996) 38-46.
  • N. Karaboga, Digital IIR Şlter design using differential evolution algorithm, EURASIP Journal on Applied Signal Processing 8 (2005) 1-9.
  • A. Kalinli, N. Karaboga, A parallel tabu search algorithm for digital Şlter design, COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 24 (2005) 1284-1298.
  • N. Karaboga, B. Cetinkaya, Design of digital FIR Şlters using differential evolution algorithm, Circuits Systems and Signal Processing Journal 25 (2006) 649-660.
  • D. J. Krusienski, W. K. Jenkins, Adaptive Şltering via particle swarm optimization, 37thAsilomar Conference on Signals Systems and Computers, 2003, pp. 571-575.
  • D. J. Krusienski, W. K. Jenkins, Particle swarm optimization for adaptive IIR Şlter structures, Congress on Evolutionary Computation, 2004, pp. 965-970.
  • A. Kalınlı, N. Karaboga, A new method for adaptive IIR Şlter design based on tabu search algorithm, International Journal of Electronics and Communication 59 (2004) 1-7.
  • S. Chen, B. L. Luk, Adaptive simulated annealing for optimization in signal processing applications, Signal Pro- cessing 79 (1999) 117-128.
  • N. Karaboga, A new design method based on artiŞcial bee colony algorithm for digital IIR Şlters, Journal of the Franklin Institute-Engineering and Applied Mathematics 346 (2009) 328-348.
  • A. P. Engelbrecht, Fundamentals of computational swarm intelligence, John Wiley & Sons Publication, Chichester, UK, 2005.
  • D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
  • D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: ArtiŞcial bee colony (ABC) algorithm, Journal of Global Optimization 39 ( 2007) 459-471.
  • R. S. Rao, S.V.L. Narasimham, M. Ramalingaraju, Optimization of distribution network conŞguration for loss reduction using artiŞcial bee colony algorithm, International Journal of Electrical Power and Energy Systems Engineering 1 (2008) 116-122.
  • A. Singh, An artiŞcial bee colony algorithm for the leaf-constraint minimum spanning tree problem, Applied Soft Computing 8 (2008) 687-697.
  • P. W. Tsai, J. S. Pan, B. Y. Liao, S. C. Chu, Interactive artiŞcial bee colony algorithm, International Symposium on Intelligent Informatics, 2008, pp. 247-251.
  • S. Hemamalini, S. P. Simon, Economic load dispatch with valve-point effect using artiŞcial bee colony algorithm, 32thNational Systems Conference, 2008, pp. 17-19.
  • T. Kurban, E. Besdok, A comparison of RBF neural network training algorithms for inertial sensor based terrain classiŞcation, Sensors Journal 9 (2009) 6312-6329.
  • D. Karaboga, B. Basturk, ArtiŞcial bee colony (ABC) optimization algorithm for solving constrained optimization problems, LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 2007, pp.789-798. [22] B. Akay, D. Karaboga, Parameter Tuning for the ArtiŞcial Bee Colony Algorithm, Lecture Notes in ArtiŞcial Intelligence, 2009, pp.608-619.