DESIGN OF TWO-DIMENSIONAL INFINITE IMPULSE RESPONSE RECURSIVE FILTERS USING HYBRID MULTIAGENT PARTICLE SWARM OPTIMIZATION

Abstract

We incorporate the optimization problem of two-dimensional infinite impulse response (IIR) recursive filters and the optimization methodology of hybrid multiagent particle swarm optimization (HMAPSO) and then apply the resultant optimized IIR filter in image processing for justifying HMAPSO robustness over other algorithm and its role in optimizing real-time situations. The design of the 2-D IIR filter is reduced to a constrained minimization problem whose robust solution is being achieved by a novel and optimal algorithm HMAPSO. This algorithm integrates the deterministic solution by the multiagent system, the particle swarm optimization (PSO) algorithm, and bee decision-making process. All agents search parallel in an equally distributed lattice-like structure to save energy and computational time as done by the bees in their hive selection process. Thus making use of deterministic search, multiagent PSO, and bee, the HMAPSO realizes the purpose of optimization. Experimental results and the application of the designed filters to focusing the defocused image show that the HMAPSO approach provides better upshots than the previous design methods.