An exponential variation based PSO for analog circuit sizing in constrained environment

• Published in: International journal of electronics and communications Vol. 187; p. 155531
• Main Authors: K.G., Shreeharsha, R.K., Siddharth, Korde, Charudatta G, M.H., Vasantha, Y.B., Nithin Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.12.2024
• Subjects: Analog circuit sizing; Constrained optimization; Low power consumption; Operational amplifiers; Particle swarm optimization (PSO); Operational amplifiers; Analog circuit sizing; Particle swarm optimization (PSO); Low power consumption; Constrained optimization
• ISSN: 1434-8411; 1618-0399
• DOI: 10.1016/j.aeue.2024.155531

This work presents an Exponential Variation based Particle Swarm Optimization (EV-PSO) algorithm to improve the convergence rate and find an optimal solution to analog circuit optimization problems in a constrained-driven environment. Existing evolutionary algorithms have a lower convergence rate leading to higher design time. This work introduces two novel parameters, ζ1 and ζ2, into the velocity update equation. These parameters dynamically vary with the number of iterations. The algorithm was implemented on the Python platform. The results have shown that, in comparison to the considered existing methods, the exponential variation of the parameters ζ1 and ζ2 in the proposed algorithms have a larger rate of convergence. The proposed EV-PSO has a convergence rate of 27 iterations, which is 57.8%, 65.38%, and 59.1% better than the conventional PSO, differential evolution (DE) and genetic algorithm (GA) respectively. The typical design obtained from the optimal solution is verified through the simulation using 45-nm CMOS technology. The optimal solution presented in this work meets the desired input specifications within the specified constrained environment. [Display omitted] •This work proposes Exponential Variation based Particle Swarm Optimization algorithm.•The Python platform is used for implementation of the EV-PSO algorithm.•Core velocity update equation updated with ζ1 and ζ2 as new variables.•The 45-nm CMOS technology was used for simulation of EV-PSO algorithm.•The proposed algorithm has a higher convergence rate compared to conventional ones.