Co-AGSA: An efficient self-adaptive approach for constrained optimization of analog IC based on the shrinking circles technique

Abstract

This paper aims to take a step forward to enhance the performance of the optimization kernel of electronic design automation (EDA) tools by coping with the existing challenges in the analog circuit sizing problems. For this purpose, a novel co-evolutionary-based optimization approach, called Co-AGSA, is proposed. In the Co-AGSA, a self-adaptive penalty technique based on the concept of the co-evolution model is incorporated into a powerful optimization algorithm, named advanced gravitational search algorithm (AGSA), to efficiently solve more realistic constrained optimization problems. The performance of the Co-AGSA approach is first evaluated by solving three constrained engineering design problems. Then, the optimization capability of the Co-AGSA-based IC sizing tool is validated using three different case studies, i.e., a two-stage op-amp, a folded-cascode op-amp and a two-stage telescopic cascode amplifier, to show the applicability of the proposed approach. The results demonstrate that the Co-AGSA gives better performance compared to other approaches in terms of efficiency, accuracy and robustness.