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.
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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)
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Oltean, G., Hintea, S., Sipos, E. (2009). A Genetic Algorithm-Based Multiobjective Optimization for Analog Circuit Design. In: Velásquez, J.D., Ríos, 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
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DOI: https://doi.org/10.1007/978-3-642-04592-9_63
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