IIR system identification using particle swarm optimization with constriction factor and inertia weight approach

In this paper a modified version of swarm intelligence technique called Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA) is applied to IIR adaptive system design problem. The proposed technique PSO-CFIWA in close similarity with Real coded Genetic Algorithm (RGA) and Particle Swarm Optimization (PSO) performs a structured randomized search of an unknown parameter within a multidimensional search space by manipulating a swarm of particles to converge to an optimal solution. PSO being a population based stochastic search method tries to maintain a proper balance between global and local search for achieving the optimum result. The exploration and exploitation of entire search space can be handled efficiently with the proposed technique PSO-CFIWA along with the benefits of overcoming the premature convergence and stagnation problems. The simulation results justify the optimization efficacy of the proposed PSO-CFIWA over RGA and PSO.