Optimal selection of components value for analog active filter design using simplex particle swarm optimization

The simplex particle swarm optimization (Simplex-PSO) is a swarm intelligent based evolutionary computation method. Simplex-PSO is the hybridization of Nedler–Mead simplex method and particle swarm optimization (PSO) without the velocity term. The Simplex-PSO has fast optimizing capability and high computational precision for high-dimensionality functions. In this paper, Simplex-PSO is employed for selection of optimal discrete component values such as resistors and capacitors for fourth order Butterworth low pass analog active filter and second order State Variable low pass analog active filter, respectively. Simplex-PSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low pass analog active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that Simplex-PSO efficiently minimizes the total design error to a greater extent in comparison with previously reported optimization techniques.