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 \zeta _{1} and \zeta _{2} . These parameters varies exponentially, within the bounds ( \zeta _{1,min} , \zeta _{2,min} ) and ( \zeta _{1,max} , \zeta _{2,max} ), with respect to the number of iterations. Initially, a higher value of \zeta _{2} and minimum value of \zeta _{1} is chosen to facilitate a global search. Once the global error ( \varepsilon _{2} ) is less than the desired value, \zeta _{1} is allowed to increase from its minimum value and \zeta _{2} is held constant at \zeta _{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.