A New Mutated Quantum-Behaved Particle Swarm Optimizer for Digital IIR Filter Design

Adaptive infinite impulse response (IIR) filters have shown their worth in a wide range of practical applications. Because the error surface of IIR filters is multimodal in most cases, global optimization techniques are required for avoiding local minima. In this paper, we employ a global optimization algorithm, Quantum-behaved particle swarm optimization (QPSO) that was proposed by us previously, and its mutated version in the design of digital IIR filter. The mechanism in QPSO is based on the quantum behaviour of particles in a potential well and particle swarm optimization (PSO) algorithm. QPSO is characterized by fast convergence, good search ability, and easy implementation. The mutated QPSO (MuQPSO) is proposed in this paper by using a random vector in QPSO to increase the randomness and to enhance the global search ability. Experimental results on three examples show that QPSO and MuQPSO are superior to genetic algorithm (GA), differential evolution (DE) algorithm, and PSO algorithm in quality, convergence speed, and robustness.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  J. Shynk.  Adaptive IIR filtering , 1989, IEEE ASSP Magazine.

[3]  P. Mars,et al.  Genetic and annealing approaches to adaptive digital filtering , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[4]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[6]  L. Coelho.  A quantum particle swarm optimizer with chaotic mutation operator , 2008 .

[7]  Dervis Karaboga,et al.  Designing digital IIR filters using ant colony optimisation algorithm , 2004, Eng. Appl. Artif. Intell..

[8]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[9]  N. Karaboga,et al.  A new method for adaptive IIR filter design based on tabu search algorithm , 2005 .

[10]  Nurhan Karaboga,et al.  Digital IIR Filter Design Using Differential Evolution Algorithm , 2005, EURASIP J. Adv. Signal Process..

[11]  W. Jenkins,et al.  Adaptive filtering via particle swarm optimization , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[12]  Shu-Hung Leung,et al.  The genetic search approach. A new learning algorithm for adaptive IIR filtering , 1996, IEEE Signal Process. Mag..

[13]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[14]  Yibo Li,et al.  The Design of IIR Digital Filter Based on Chaos Particle Swarm Optimization Algorithm , 2008, 2008 Second International Conference on Genetic and Evolutionary Computing.

[15]  Bing Lam Luk,et al.  Digital IIR Filter Design Using Adaptive Simulated Annealing , 2001, Digit. Signal Process..

[16]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[17]  Qiang Ma,et al.  Genetic algorithms applied to the adaptation of IIR filters , 1996, Signal Process..

[18]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).