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.

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

“…Although they did not evolve robust circuits in their work, they suggested that a consideration of manufacturing errors (tolerances) of component values is needed and that in evolvable hardware, the component errors intrinsic in physical components can be absorbed during the evolutionary process. Dib and El-Asir [53] proposed using a symbiotic organisms search (SOS) algorithm to determine the values of the passive components (resistors and capacitors) used in active filters. They compared SOS performance with those obtained using other optimization methods, such as particle swarm optimization, seeker optimization algorithms, and differential evolution, and found that SOS works best.…”

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

“…In [10,11], SOS has been applied to solve the optimal power flow and the economic emission load dispatch problems. Moreover, SOS has been successfully applied in the optimal design of linear and circular antenna arrays [12,13], in solving the load frequency control problem [14], in structure optimization problems [15], in solving unconstrained function optimization [16], in the design of an improved 3D Turbo Code [17], in the optimal design of analog active filters [18], in the synthesis of elliptical antenna arrays [19], and other engineering areas [20][21][22][23][24]. The results indicated that SOS gives very good results and is very competitive with the state of the art for the solution of these problems, which motivated this work.…”

Symbiotic organisms search algorithm for optimal design of CMOS two‐stage op‐amp with nulling resistor and robust bias circuit

“…In [18], the SOS algorithm is applied to determine the coefficients of an optimal fractional order digital integrator. The SOS algorithm has been adopted to design an optimal active analogue filter in [19]. To enhance the power supply stability, the SOS algorithm is explored in [20] for the optimum placement of distributed generators in the distributed network.…”