An Efficient Craziness Based Particle Swarm Optimization Technique for Optimal IIR Filter Design

In this paper an improved version of Particle Swarm Optimization (PSO) called Craziness based PSO (CRPSO) is considered as an efficient optimization tool for designing digital Infinite Impulse Response (IIR) filters. Apart from gaining better control on cognitive and social components of conventional PSO, the CRPSO dictates better performance due to incorporation of craziness parameter in the velocity equation of PSO. This modification in the velocity equation not only ensures the faster searching in the multidimensional search space but also the solution produced is very close to the global optimal solution. The effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, Real coded Genetic Algorithm (RGA) and conventional Particle Swarm Optimization (PSO) with a superior CRPSO based outcome for the designed 8th order IIR low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filters. Simulation results affirm that the proposed CRPSO algorithm outperforms its counterparts not only in terms of quality output, i.e., sharpness at cut-off, pass band ripple and stop band attenuation but also in convergence speed with assured stability.