Optimal design of analog active filters using symbiotic organisms search

Abstract

This paper investigates the optimal design of analog active filters using the symbiotic organisms search (SOS) algorithm. Symbiotic organisms search is a newly proposed global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the 3 common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other methods, SOS is free of tuning parameters, which makes it an attractive optimization method. Here, SOS is used to determine the values of the passive components (resistors and capacitors) used in active filters. Two active filter topologies, fourth-order Butterworth low-pass filter and second-order state variable low-pass filter, are considered. The components values are chosen from several standard industrial series (E12, E24, and E96). The results obtained using SOS are compared with those obtained using several optimization methods, like particle swarm optimization, seeker optimization algorithm, differential evolution, and other ones. It is shown that the SOS is a robust straightforward evolutionary algorithm that can outperform other well-known methods.