Metaheuristic-based Optimization Techniques for Optimal Analog Filter Sizing

Analog filters design depends strongly on the appropriate selection of discrete components (Resistor and Capacitors). The challenge is to find the values which allow an optimal filters sizing with high accuracy. Some metaheuristics have proved a capacity to treat such problem effectively. In this paper, we present an application of two metaheuristic-based optimization techniques in order to select optimal values of resistors and capacitors from different manufactured series (E12, E24, E48, E96 and E192) to satisfy the filters design criteria. The Ant Colony Optimization technique (ACO) and the Genetic Algorithm (GA) are applied for the optimal sizing of two analog filters named a second order low-pass state variable filter and a high pass sallen-key filter. PSPICE simulations are given to validate the obtained results/performances.