An Error Bound Particle Swarm Optimization for Analog Circuit Sizing

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Shreeharsha, K.G., Siddharth, R.K., Vasantha, M.H., Nithin Kumar, Y.B.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analog circuit sizing; Analog circuits; constrained optimization; Convergence; Linear programming; Mathematical models; Optimization; Parameter modification; Particle swarm optimization; Particle swarm optimization (PSO); Sizing; Sociology; Statistics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3385491

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.