Digital FIR Filter Design Using Hybrid Random Particle Swarm Optimization with Differential Evolution
This paper presents a novel approach of designing linear phase FIR low pass and high pass filter using Random PSO in hybrid with DE known as Random PSODE (RPSODE). In this paper, the Random PSO is used which utilises the weighted particle to guide the search direction for both explorative and exploitative searches. Differential evolution (DE) is one of the very fast and robust evolutionary algorithms which has shown superior performance for continuous global optimization; uses differential information to guide its search direction but sometime causes instability problem; whereas, PSO is a robust, population based stochastic search technique but has the problem of sub-optimality. This paper efficiently combines the Random PSO and DE so as to overcome the disadvantages faced by both the algorithms individually and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show the superiority of RPSODE in global convergence properties and local search ability, and prove it to be a promising candidate for designing the FIR filters. RPSODE outperforms PSO, DE, and PSODE not only in magnitude response but in the convergence speed as well.
fir filter de pso random psode psode convergence magnitude response evolutionary optimization technique