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

Wei Fang, Jun Sun and Wenbo Xu Centre of Intelligent and High Performance Computing, School of Information Technology, Jiangnan University No 1800, Lihu Avenue, Wuxi, China, 214122 {wxfangwei, sunjun_wx, xwb_sytu}@hotmail.com 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.