In this paper, a linear phase finite impulse response (FIR) high pass (HP) digital filter is designed using a recently proposed heuristic search algorithm called gravitational search algorithm (GSA). Various evolutionary techniques like conventional particle swarm optimization (PSO), differential evolution (DE) and the proposed gravitational search algorithm (GSA) have been applied for the optimal design of linear phase FIR HP filters. Real coded genetic algorithm (RGA) has also been adopted for the sake of comparison. In GSA, agents are considered as objects and their performances are measured by their masses. All these objects attract each other by the gravity forces and these forces cause a global movement of all objects towards the objects with heavier masses. Hence, masses cooperate amongst each other using a direct form of communication through gravitational forces. The heavier masses (which correspond to better solutions) move more slowly than the lighter ones. This guarantees the exploitation step of the algorithm. GSA is apparently free from getting trapped at local optima and premature convergence. Extensive simulation results justify the superiority and optimization efficacy of the GSA over the afore-mentioned optimization techniques for the solution of the multimodal, non-differentiable, highly non-linear, and constrained filter design problems.
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