Synthesis of Analog Circuits by Genetic Algorithms and their Optimization by Particle Swarm Optimization

Intelligent Systems for Automated Learning and Adaptation Pub Date : 1900-01-01 DOI:10.4018/978-1-60566-798-0.ch008

E. Tlelo-Cuautle, I. Guerra-Gómez, C. García, M. Duarte-Villaseñor

引用次数: 15

Abstract

This chapter shows the application of particle swarm optimization (PSO) to size analog circuits which are synthesized by a genetic algorithm (GA) from nullor-based descriptions. First, a historical description of the development of automatic synthesis techniques to design analog circuits is presented. Then, the synthesis of analog circuits by applying a GA at the transistor level of abstraction is demonstrated. After that, the authors present the proposed multi-objective (MO) PSO algorithm which makes calls to the circuit simulator HSPICE to evaluate performances until optimal sizes of the transistors are found by using standard CMOS technology of 0.35μm of integrated circuits. Finally, the MO-PSO algorithm is compared with NSGA-II, and some open problems oriented to circuit synthesis and sizing are briefly discussed. DOI: 10.4018/978-1-60566-798-0.ch008

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基于遗传算法的模拟电路合成及其粒子群优化

本章展示了粒子群优化(PSO)在基于零值描述的遗传算法(GA)合成的模拟电路尺寸中的应用。首先，介绍了模拟电路设计中自动合成技术的发展历史。然后，演示了在晶体管抽象级应用遗传算法合成模拟电路。在此基础上，作者提出了一种多目标粒子群算法，该算法通过调用电路模拟器HSPICE来评估性能，直到使用0.35μm集成电路的标准CMOS技术找到晶体管的最佳尺寸。最后，将MO-PSO算法与NSGA-II算法进行了比较，并简要讨论了一些面向电路综合和尺寸确定的开放性问题。DOI: 10.4018 / 978 - 1 - 60566 - 798 - 0. - ch008

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Intelligent Systems for Automated Learning and Adaptation

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