A Hybrid Multipopulation Algorithm for Efficient Analog Circuit Optimization

As the complexity of analog circuit optimization problems increases, existing optimization algorithms struggle with intricate circuit specifications. In this paper, we propose a hybrid multi-population evolutionary algorithm framework that integrates and enhances GA, DE, and PSO. We introduce a dynamic fitness function to address multi-constraint, multi-objective problems, and design a beta-distribution-based crossover operator with grouping to handle correlations between design variables and the highly nonlinear, locally sensitive nature of circuit metrics. Additionally, we implement an asymmetric crowding mechanism that considers nominal variables to maintain population diversity and develop a multi-population cooperation strategy to improve both convergence speed and solution quality. Our framework is validated on four analog circuits: a Low Dropout Regulator, a Two-Stage Amplifier, a Four-Stage Amplifier, and a Rail-to-rail Class AB Amplifier. Results demonstrate that our algorithm achieves faster convergence and superior solutions, leading to better performance of analog circuits and significant improvements in key multi-objective metrics such as hypervolume (HV) and dominance coverage. These confirm the effectiveness and efficiency of the proposed framework in solving complex analog circuit optimization problems.