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A hierarchy in mutation of genetic algorithm and its application to multi-objective analog/RF circuit optimization

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Abstract

This paper presents a multi-objective analog circuit design optimization tool using genetic algorithm based on hierarchical mutation scheme. The idea is to improve the convergence and diversity of genetic algorithm by incorporating hierarchy during polynomial mutation operation. In this regard, a theoretical framework of proposed genetic algorithm is presented using Markov chain principle. To investigate the effectiveness of hierarchy in polynomial mutation operator, the scheme is compared with six different mutation strategies. Experiments are performed for different function evaluations to evaluate the performance of hierarchical polynomial mutation operator. Further, to showcase the improvement in genetic algorithm, numerous experiments are performed on twelve different test functions and two design examples. The proposed genetic algorithm shows competitive performance over other standard optimization techniques in terms of both convergence and diversity of solutions.

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Notes

  1. Polynomial mutation operation is considered as it is carried out using hierarchical scheme.

  2. Here, transducer power gain (\(G_T\) ) is considered as gain, which can be represented as, \(G_T\) = \(|S21|^2\) or \(G_T\) = 20log|S21| in dB as the source and load impedances are matched to the reference impedance during the design of LNA.

References

  1. Mandal, P., & Visvanathan, V. (2001). Cmos op-amp sizing using a geometric programming formulation. IEEE Transactions on Computer-Aided Design of Integrated circuits and systems, 20(1), 22–38.

    Article  Google Scholar 

  2. Martens, E., & Gielen, G. (2008). Classification of analog synthesis tools based on their architecture selection mechanisms. Integration, the VLSI Journal, 41(2), 238–252. https://doi.org/10.1016/j.vlsi.2007.06.001.

    Article  Google Scholar 

  3. Michal, J., & Dobes, J. (2007). Electronic circuit design using multiobjective optimization. In 2007 50th Midwest symposium on circuits and systems (pp. 734–737). IEEE.

  4. Koza, J. R., Bennett, F. H., Andre, D., Keane, M. A., & Dunlap, F. (1997). Automated synthesis of analog electrical circuits by means of genetic programming. IEEE Transactions on Evolutionary Computation, 1(2), 109–128. https://doi.org/10.1109/4235.687879.

    Article  Google Scholar 

  5. Das, S., Mallipeddi, R., & Maity, D. (2013). Adaptive evolutionary programming with p-best mutation strategy. Swarm and Evolutionary Computation, 9, 58–68. https://doi.org/10.1016/j.swevo.2012.11.002.

    Article  Google Scholar 

  6. Fakhfakh, M., Cooren, Y., Sallem, A., Loulou, M., & Siarry, P. (2010). Analog circuit design optimization through the particle swarm optimization technique. Analog Integrated Circuits and Signal Processing, 63(1), 71–82.

    Article  Google Scholar 

  7. Kubař, M., & Jakovenko, J. (2013). A powerful optimization tool for analog integrated circuits design. Radioengineering, 22(3), 921.

    Google Scholar 

  8. Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAM Journal on Computing, 2(2), 88–105.

    Article  MathSciNet  MATH  Google Scholar 

  9. Cheng, R., Gen, M., & Tsujimura, Y. (1996). A tutorial survey of job-shop scheduling problems using genetic algorithms: I. Representation. Computers & industrial engineering, 30(4), 983–997.

    Article  Google Scholar 

  10. Grefenstette, J., Gopal, R., Rosmaita, B., & Van Gucht, D. (1985). Genetic algorithms for the traveling salesman problem. In Proceedings of the first international conference on genetic algorithms and their applications, Lawrence Erlbaum, New Jersey (pp. 160–168).

  11. Changdar, C., Mahapatra, G., & Pal, R. K. (2014). An efficient genetic algorithm for multi-objective solid travelling salesman problem under fuzziness. Swarm and Evolutionary Computation, 15, 27–37. https://doi.org/10.1016/j.swevo.2013.11.001. http://www.sciencedirect.com/science/article/pii/S2210650213000679.

  12. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.

    Article  Google Scholar 

  13. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  14. Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach. Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601.

    Article  Google Scholar 

  15. Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). New York: Springer.

    MATH  Google Scholar 

  16. Suzuki, J. (1995). A markov chain analysis on simple genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 25(4), 655–659. https://doi.org/10.1109/21.370197.

    Article  Google Scholar 

  17. Deb, K., Mohan, M., & Mishra, S. (2003). A fast multi-objective evolutionary algorithm for finding well-spread pareto-optimal solutions. KanGAL Report, 2003002, 1–18.

    Google Scholar 

  18. Iosifescu, M. (2014). Finite Markov processes and their applications. North Chelmsford: Courier Corporation.

    Google Scholar 

  19. Rudolph, G. (1994). Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5(1), 96–101.

    Article  MathSciNet  Google Scholar 

  20. Tweedie, R. L. (1974). R-theory for Markov chains on a general state space I: Solidarity properties and r-recurrent chains. The Annals of Probability, 2(5), 840–864.

    Article  MathSciNet  MATH  Google Scholar 

  21. Doob, J. L., & Doob, J. L. (1953). Stochastic processes (Vol. 7, No. 2). New York: Wiley.

    MATH  Google Scholar 

  22. Jiang, S., Ong, Y.-S., Zhang, J., & Feng, L. (2014). Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Transactions on Cybernetics, 44(12), 2391–2404.

    Article  Google Scholar 

  23. Fonseca, C. M., Paquete, L., & López-Ibánez, M. (2006). An improved dimension-sweep algorithm for the hypervolume indicator. In: 2006 IEEE international conference on evolutionary computation (pp. 1157–1163). IEEE.

  24. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning (1st ed.). Boston, MA: Addison-Wesley Longman Publishing Co., Inc.

    MATH  Google Scholar 

  25. Lee, C.-Y., & Yao, X. (2001). Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation, 2001 (Vol. 1, pp. 568–575). IEEE.

  26. Chellapilla, K. (1998). Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation, 2(3), 91–96.

    Article  Google Scholar 

  27. Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82–102. https://doi.org/10.1109/4235.771163.

    Article  Google Scholar 

  28. Michalewicz, Z., & Janikow, C. Z. (1991). Handling constraints in genetic algorithms. In ICGA (pp. 151–157).

  29. Bäck, T., & Schwefel, H.-P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), 1–23.

    Article  Google Scholar 

  30. Li, H., & Zhang, Q. (2009). Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2), 284–302.

    Article  Google Scholar 

  31. Durillo, J., Nebro, A., Luna, F., & Alba, E. (2009). On the effect of the steady-state selection scheme in multi-objective genetic algorithms. In 5th International conference, EMO 2009. Lecture notes in computer science (Vol. 5467, pp. 183–197). Berlin: Springer.

  32. Rutenbar, R. A., Gielen, G., & Antao, B. (2002). Computer-aided design of analog integrated circuits and systems. Piscataway: IEEE Press.

    Book  Google Scholar 

  33. Boyd, S. P., Lee, T. H., et al. (2001). Optimal design of a CMOS op-amp via geometric programming. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 20(1), 1–21.

    Article  Google Scholar 

  34. Harjani, R., Rutenbar, R. A., & Carley, L. R. (1989). Oasys: A framework for analog circuit synthesis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 8(12), 1247–1266.

    Article  Google Scholar 

  35. Ochotta, E. S., Rutenbar, R. A., & Carley, L. R. (1996). Synthesis of high-performance analog circuits in ASTRX/OBLX. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 15(3), 273–294.

    Article  Google Scholar 

  36. Gielen, G., Walscharts, H., & Sansen, W. (1989). Analog circuit design optimization based on symbolic simulation and simulated annealing. In Solid-state circuits conference, 1989. ESSCIRC ’89. Proceedings of the 15th European (pp. 252–255).

  37. Kruiskamp, W., & Leenaerts, D. (1995). Darwin: CMOS opamp synthesis by means of a genetic algorithm. In Proceedings of the 32Nd annual ACM/IEEE design automation conference, DAC ’95 (pp. 433–438). New York, NY: ACM .

  38. Karaboga, N., Kalinli, A., & Karaboga, D. (2004). Designing digital IIR filters using ant colony optimisation algorithm. Engineering Applications of Artificial Intelligence, 17(3), 301–309.

    Article  MATH  Google Scholar 

  39. Tsai, J.-T., Chou, J.-H., & Liu, T.-K. (2006). Optimal design of digital IIR filters by using hybrid taguchi genetic algorithm. IEEE Transactions on Industrial Electronics, 53(3), 867–879.

    Article  Google Scholar 

  40. Karaboga, N., & Cetinkaya, B. (2006). Design of digital FIR filters using differential evolution algorithm. Circuits, Systems, and Signal Processing, 25(5), 649–660.

    Article  MathSciNet  MATH  Google Scholar 

  41. Gentili, P., Piazza, F., & Uncini, A. (1995). Efficient genetic algorithm design for power-of-two fir filters. In 1995 International conference on acoustics, speech, and signal processing, 1995. ICASSP-95 (Vol. 2, pp. 1268–1271). IEEE.

  42. Cadence inc., products: Composer, virtuoso, diva, neocircuit, neocell, ultrasim, ncsim.

  43. Analog design automation, inc.

  44. del Mar Hershenson, M., Boyd, S. P., & Lee, T. H. (1998). Gpcad: A tool for CMOS op-amp synthesis. In Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design (pp. 296–303). ACM.

  45. Weber, T. O., & Van Noije, W. A. (2011). Analog design synthesis method using simulated annealing and particle swarm optimization. In Proceedings of the 24th symposium on Integrated circuits and systems design (pp. 85–90). ACM.

  46. Barros, M., Guilherme, J., & Horta, N. (2010). Analog circuits optimization based on evolutionary computation techniques. Integration, the VLSI Journal, 43(1), 136–155.

    Article  MATH  Google Scholar 

  47. Rabuske, T., & Fernandes, J. (2014). Noise-aware simulation-based sizing and optimization of clocked comparators. Analog Integrated Circuits and Signal Processing, 81(3), 723–728.

    Article  Google Scholar 

  48. Allen, P. E., & Holberg, D. R. (2002). CMOS analog circuit design. Oxford: Oxford University Press.

    Google Scholar 

  49. Cadence design systems, inc., virtuoso schematic editor. Available at https://www.cadence.com/content/dam/cadence-www/global/en_us/documents/tools/custom-ic-analog-rf-design/virtuoso-vse-fam-ds.pdf.

  50. Andreani, P., & Sjoland, H. (2001). Noise optimization of an inductively degenerated cmos low noise amplifier. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 48(9), 835–841.

    Article  Google Scholar 

  51. Nebro, A. J., Luna, F., Alba, E., Dorronsoro, B., Durillo, J. J., & Beham, A. (2008). AbYSS: Adapting scatter search to multiobjective optimization. IEEE Transactions on Evolutionary Computation, 12(4), 439–457.

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology Guwahati, Guwahati, India

    Satyabrata Dash, Deepak Joshi & Gaurav Trivedi

  2. LNM Institute of Information Technology, Jaipur, India

    Ayushparth Sharma

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  1. Satyabrata Dash
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  2. Deepak Joshi
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Correspondence to Satyabrata Dash.

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Dash, S., Joshi, D., Sharma, A. et al. A hierarchy in mutation of genetic algorithm and its application to multi-objective analog/RF circuit optimization. Analog Integr Circ Sig Process 94, 27–47 (2018). https://doi.org/10.1007/s10470-017-1090-4

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