Abstract
Multiple, often conflicting objectives are specific to analog design. This paper presents a multiobjective optimization algorithm based on GA for design optimization of analog circuits. The fitness of each individual in the population is determined using a multiobjective ranking method. The algorithm found a set of feasible solutions on the Pareto front. Thus, the circuit designers can explore more possible solutions, choosing the final one according to further preferences/constraints. The proposed algorithm was shown to produce good solutions, in an efficient manner, for the design optimization of a CMOS amplifier, for two different sets of requirements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Greenwood, G.W., Tyrrell, A.M.: Introduction to Evolvable Hardware. IEEE Press Series on Computational Intelligence. Wiley&Sons Inc., Los Alamitos (2007)
Nicosia, G., Rinaudo, S., Sciacca, E.: An Evolutionary Algorithm-based Approach to Robust Analog Circuit Design using Constrained Multi-objective Optimization. Knowledge-based Systems 21(3), 175–183 (2008)
Zitzler, E., Laumanns, M., Bleuler, S.: A Tutorial on Evolutionary Multiobjective Optimization. In: Proceedings of the Workshop on Multiple Objective Metaheuristics, pp. 3–38. Springer, Heidelberg (2004)
Yaser, M.A.K., Badar, K., Faisal, T.: Multiobjective Optimization Tool for a Free Structure Analog Circuits Design Using Genetic Algorithms and Incorporating Parasitics. In: Proc. of the Conference Companion on Genetic and Evolutionary computation, pp. 2527–2534 (2007) ISBN 978-1-59593-698-1
Goh, C., Li, Y.: Multi-objective Synthesis of CMOS Operational Amplifiers using a Hybrid Genetic Algorithm. In: Proc.of the 4th Asia-Pacific Conf. on Simulated Evolution and Learning, pp. 214–218 (2002)
Eeckelaert, T., Schoofs, R., Gielen, G., Steyaert, M., Sansen, W.: Hierarchical Bottom-up Analog Optimization Methodology Validated by a Delta-Sigma A/D Converter Design for the 802.11a/b/g standard. In: Design Automation Conf., 43rd ACM/IEEE, pp. 25–30 (2006)
Boyd, S., Vandeberghe, L.: Introduction to Convex Optimization with Engineering Application. Stanford University (1999)
Oltean, G.: FADO - A CAD Tool for Analog Modules. In: Proc. of the International Conference on Computer as a Tool, EUROCON 2005, Belgrade, pp. 515–518 (2005) ISBN 1-4244-0050-3, IEEE catalog number: 05EX1255C
Oltean, G.: Fuzzy Logic Toolbox 2, User’s Guide, on line version, The Math Works, Inc. (2007)
Jang, R.J.-S.: ANFIS, Adaptive-Network-Based Fuzzy Inference System. IEEE Transaction on System, Man, and Cybernetics 23(3), 665–685 (1993)
Cecilia, R., Machado, J.A.T., Cunha, J.B., Pires, E.J.S.: Evolutionary Computation in the Design of Logic Circuits. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1664–1669 (2007)
Pohlheim, H.: GEATbx Introduction to Evolutionary Algorithms: Overview, Methods and Operators, version 3.7 (November 2005), http://www.geatbx.com/
Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm in continuous parameter optimization. In: Evolutionary Computation, vol. 1(1), pp. 25–49 (1993) ISSN 1063-6560
Oltean, G.: Fuzzy Techniques in Analog Circuit Design. WSEAS Transactions on Circuits and Systems 7(5), 402–415 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oltean, G., Hintea, S., Sipos, E. (2009). A Genetic Algorithm-Based Multiobjective Optimization for Analog Circuit Design. In: Velásquez, J.D., RÃos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_63
Download citation
DOI: https://doi.org/10.1007/978-3-642-04592-9_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04591-2
Online ISBN: 978-3-642-04592-9
eBook Packages: Computer ScienceComputer Science (R0)