Design of optimal linear phase FIR high pass filter using craziness based particle swarm optimization technique

In this paper, an optimal design of linear phase digital high pass fir filter using craziness based particle swarm optimization (crpso) approach has been presented. fir filter design is a multi-modal optimization problem. the conventional gradient based optimization techniques are not efficient for such multi-modal optimization problem as they are susceptible to getting trapped on local optima. given the desired filter specifications to be realized, the crpso algorithm generates a set of optimal filter coefficients and tries to meet the desired specifications. in birds’ flocking or fish schooling, a bird or a fish often changes directions suddenly. this is described by using a ‘‘craziness’’ factor and is modeled in the crpso technique. in this paper, the realizations of the crpso based optimal fir high pass filters of different orders have been performed. the simulation results have been compared to those obtained by the well accepted classical optimization algorithm such as parks and mcclellan algorithm (pm), and evolutionary algorithms like real coded genetic algorithm (rga), and conventional particle swarm optimization (pso). the results justify that the proposed optimal filter design approach using crpso outperforms pm, rga and pso, in the optimal characteristics of frequency spectrums.