An Efficient Hybrid SIMBO-GA Approach to Design FIR Low Pass Filter

摘要

In this paper a narrative approach for designing FIR low pass filter is presented by practicing hybrid technique of Swine Influenza Model based Optimization (SIMBO) and Genetic Algorithm (GA). Premature convergence was the major difficulty faced by SIMBO algorithm individually in FIR filter design. To address this problem, a hybrid SIMBO-GA is proposed in this paper. GA is used to help SIMBO escape from local optima and prevent premature convergence. Results are presented and compared in term of magnitude response with Differential Evolution Particle Swarm Optimization (DEPSO), Genetic Lbest Particle Swarm Optimization with Dynamically Varying Neighbourhood (GLPSO DVN). A comparison of simulation results divulges that SIMBO-GA seems to be promising tool for FIR filter design.