Gravitational search algorithm in digital FIR low pass filter design

This paper proposes one novel design method for FIR low pass filter design using a recently proposed heuristic search algorithm called gravitational search algorithm (GSA). Various swarm based algorithms like real coded genetic algorithm (RGA), 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 low pass digital filter. In GSA, agents are considered as objects and their performance is measured by their masses. All these objects attract each other by gravity forces, and these forces produce a global movement of all objects towards the objects with heavier masses. Hence, masses cooperate using a direct form of communication through gravitational forces. The heavy masses (which correspond to good solutions) move more slowly than 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 show the superiority and optimization efficacy of the GSA over the afore-mentioned optimization techniques for the solution of the multimodal, non-differentiable, and constrained filter design problems.