An Error Bound Particle Swarm Optimization for Analog Circuit Sizing - K. G. Shreeharsha; R. K. Siddharth; M. H. Vasantha; Y. B. Nithin Kumar (IEEE Access, 2024)
An Error-Bound Particle Swarm Optimization (EB-PSO) is proposed in this work. The objective function is evaluated for each particle in each iteration. The velocity update equation is modified by introducing two new parameters $ζ _{1}$ and $ζ _{2}$ . These parameters varies exponentially, within the bounds ( $ζ _{1,min}$ , $ζ _{2,min}$ ) and ( $ζ _{1,max}$ , $ζ _{2,max}$ ), with respect to the number of iterations. Initially, a higher value of $ζ _{2}$ and minimum value of $ζ _{1}$ is chosen to facilitate a global search. Once the global error ( $ε _{2}$ ) is less than the desired value, $ζ _{1}$ is allowed to increase from its minimum value and $ζ _{2}$ is held constant at $ζ _{2,max}$ . This leads to local exploitation of the search space. The proposed algorithm is implemented on Python platform. To verify the effectiveness of the proposed EB-PSO algorithm in analog circuit sizing, a case study on the performance and optimization of two-stage op-amp is presented, whose validation is done in Cadence-Virtuoso environment at 45-nm CMOS technology. The results show that the proposed EB-PSO algorithm converges in 11 iterations for two-stage op-amp, whereas it takes 23, 29, and 41 iterations to converge for conventional GA, DE, and PSO algorithms respectively.