Analog active filter design using a multi objective genetic algorithm

The selection of the correct values for passive elements, resistors, and capacitors, is an important task in analog active filter design. The classic method of choosing passive elements is a difficult task and can lead to errors. To reduce the incidence of error and human effort evolutionary optimization techniques are used to select the values of capacitors and resistors. However, due to the single objective optimization technique, these are not well suited to optimize different filter parameters. For this reason, the performance of a multi-objective genetic algorithm named non-dominated sorting genetic algorithm II (NSGA-II) against the different single objective algorithms is evaluated. Two analog active filters: A fourth order Butterworth and a second order state variable filter with the operational amplifiers in their cores are used for testing purposes. In both cases two different objects are chosen along with eight components as variables to be optimized. The component values are compatible with the E12, E24 and E96 series using NSGA-II. The computation results are better in terms of design error and allow for better resistor and capacitor choice. To reach the same or better results the NSGA-II needs fewer generations compared with other genetic algorithms for this problem.