An Error Bound Particle Swarm Optimization for Analog Circuit Sizing

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 ζ1and ζ2. These parameters varies exponentially, within the bounds (ζ1,min, ζ2,min) and (ζ1,max, ζ2,max), with respect to the number of iterations. Initially, a higher value of ζ2and minimum value of ζ1is chosen to facilitate a global search. Once the global error (ε2) is less than the desired value, ζ1is allowed to increase from its minimum value and ζ2is held constant at ζ2,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.