Particle Swarm Optimization with Aging Leader and Challengers for Optimal Design of Analog Active Filters

Due to the manufacturing limitations, the task of optimal analog active filter design by hand is very difficult. Evolutionary computation may be a competent implement for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique used is particle swarm optimization (PSO) with Aging Leader and Challenger (ALC-PSO). ALC-PSO performs the dual-task of efficiently selecting the component values as well as minimizing the total design errors of low pass 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 ALC-PSO efficiently minimizes the total design error with respect to previously used optimization techniques.