Component value selection for active filters based on minimization of GSP and E12 compatible using Grey Wolf and Particle Swarm Optimization

The component value selection for active analogue filter design is an important issue to improve the performances and to make compatible with existing parameters value. The Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are efficient intelligent evolutionary algorithms for solving optimization problems. Both techniques are used to minimize the total design error of a 4th order Butterworth low pass active filter. This would be realized with component values which are compatible with E12 series. In addition, stability of filter is guaranteed by minimization of gain sensitivity product. By considering the minimization of Gain Sensitivity Product (GSP), the sixteen variables of objective function are reduced to eight variables which speed up the iteration process. The simulation results prove the efficiency of algorithms for the design of analogue active filter by optimizing the component values based on E12 compatible with minimization of GSP by minimising the design error.