Improving performance of genetic algorithm in circuit optimization

Alexander Zemliak; C. O. Martinez

doi:10.3103/S0735272724100054

Authors

Alexander Zemliak Autonomous University of Puebla, Puebla, Mexico https://orcid.org/0000-0002-9158-1618
C. O. Martinez Autonomous University of Puebla, Puebla, Mexico

DOI:

https://doi.org/10.3103/S0735272724100054

Keywords:

electronic circuit optimization, genetic algorithm, GA, generalized optimization approach, control vector, set of optimization strategies

Abstract

This work is devoted to optimizing electronic circuits by modifying the genetic algorithm (GA) based on the previously developed idea of generalized circuit optimization. In this case, it is possible to overcome one of the main disadvantages of GA, namely, premature convergence to local minima, and significantly improve the quality of minimization. In this case, the updated GA generates a set of populations determined by different generalized optimization strategies. A control vector introduced within the generalized optimization framework determines fitness functions and chromosome parameters, such as length and structure. The control vector specifies the number of independent variables of the optimization problem and is the main element for selecting the most promising optimization strategies. Complex strategies obtained by combining different basic strategies can improve the accuracy of the optimization problem and reduce the number of required generations. As a result, it is possible to improve the quality of the resulting solution and significantly reduce the computer time needed to optimize the electronic circuit.

References

G. Stehr, M. Pronath, F. Schenkel, H. Graeb, K. Antreich, “Initial sizing of analog integrated circuits by centering within topology-given implicit specifications,” in ICCAD-2003. International Conference on Computer Aided Design (IEEE Cat. No.03CH37486), 2003, pp. 241–246, doi: https://doi.org/10.1109/ICCAD.2003.159696.
M. delM. Hershenson, S. P. Boyd, T. H. Lee, “Optimal design of a CMOS op-amp via geometric programming,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 20, no. 1, pp. 1–21, 2001, doi: https://doi.org/10.1109/43.905671.
J. W. Bandler, R. M. Biernacki, S. H. Chen, P. A. Grobelny, R. H. Hemmers, “Space mapping technique for electromagnetic optimization,” IEEE Trans. Microw. Theory Tech., vol. 42, no. 12, pp. 2536–2544, 1994, doi: https://doi.org/10.1109/22.339794.
S. Koziel, J. W. Bandler, K. Madsen, “Space-mapping-based interpolation for engineering optimization,” IEEE Trans. Microw. Theory Tech., vol. 54, no. 6, pp. 2410–2421, 2006, doi: https://doi.org/10.1109/TMTT.2006.875298.
P. Grabusts, J. Musatovs, V. Golenkov, “The application of simulated annealing method for optimal route detection between objects,” Procedia Comput. Sci., vol. 149, pp. 95–101, 2019, doi: https://doi.org/10.1016/j.procs.2019.01.112.
B. Liu et al., “Analog circuit optimization system based on hybrid evolutionary algorithms,” Integration, vol. 42, no. 2, pp. 137–148, 2009, doi: https://doi.org/10.1016/j.vlsi.2008.04.003.
H. Nenavath, R. K. Jatoth, “Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking,” Appl. Soft Comput., vol. 62, pp. 1019–1043, 2018, doi: https://doi.org/10.1016/j.asoc.2017.09.039.
L. Xu, B. Song, M. Cao, “An improved particle swarm optimization algorithm with adaptive weighted delay velocity,” Syst. Sci. Control Eng., vol. 9, no. 1, pp. 188–197, 2021, doi: https://doi.org/10.1080/21642583.2021.1891153.
F. Long, B. Jin, H. Xu, J. Wang, J. Bhola, S. N. Shavkatovich, “Research on multi-objective optimization of smart grid based on particle swarm optimization,” Electrica, vol. 23, no. 2, pp. 222–230, 2022, doi: https://doi.org/10.5152/electrica.2022.22047.
Y.-C. Chuang, C.-T. Chen, C. Hwang, “A simple and efficient real-coded genetic algorithm for constrained optimization,” Appl. Soft Comput., vol. 38, pp. 87–105, 2016, doi: https://doi.org/10.1016/j.asoc.2015.09.036.
A. A. Rahmani Hosseinabadi, J. Vahidi, B. Saemi, A. K. Sangaiah, M. Elhoseny, “Extended genetic algorithm for solving open-shop scheduling problem,” Soft Comput., vol. 23, no. 13, pp. 5099–5116, 2019, doi: https://doi.org/10.1007/s00500-018-3177-y.
V. G. Kobak, V. M. Porksheyan, A. G. Jukovskiy, R. S. Shkabriy, “Features of using a two-point crossover for solving an inhomogeneous minimax problem modified by the Goldberg model,” J. Phys. Conf. Ser., vol. 2131, no. 222121, 2021, doi: https://doi.org/10.1088/1742-6596/2131/2/022121.
A. Zemliak, “Analog circuit optimization on basis of control theory approach,” COMPEL Int. J. Comput. Math. Electr. Electron. Eng., vol. 33, no. 6, pp. 2180–2204, 2014, doi: https://doi.org/10.1108/COMPEL-10-2013-0324.
I. S. Kashirskiy, Y. K. Trokhimenko, General Optimization for Electronic Circuits. Kiev: Tekhnika, 1979.
E. S. Ochotta, R. A. Rutenbar, L. R. Carley, “Synthesis of high-performance analog circuits in ASTRX/OBLX,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 15, no. 3, pp. 273–294, 1996, doi: https://doi.org/10.1109/43.489099.
L. D. Davis, Handbook of Genetic Algorithms. New York: Thomson Publishing Group, 1991.
P. Antognetti, G. Massobrio, Semiconductor Device Modeling with SPICE. New York: McGraw-Hill, 1993.