Skip to main content
SpringerLink
Log in

StormOptimus: A Single Objective Constrained Optimizer Based on Brainstorming Process for VLSI Circuits

  • Chapter
  • First Online:
Brain Storm Optimization Algorithms

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 23))

  • 453 Accesses

Abstract

This chapter presents the main aspects and implications of design optimization of electronic circuits using a general purpose single objective optimization approach based on the brainstorming process, which is referred as StormOptimus. The single objective optimization framework is utilized for sizing of four amplifiers, and one VLSI power grid circuit. During optimization, the problem specific information required for each circuit is kept to minimal, which consists of input specifications, design parameter ranges and a fitness function that represents the circuit’s desired behavior. Several experiments are performed on these circuits to demonstrate the effectiveness of the proposed approach. It is observed that a satisfactory design is achieved for each of the five circuits by using the proposed single objective optimization framework.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Glob. Optim. 31(4), 635–672 (2005)

    Article  MathSciNet  Google Scholar 

  2. Allen, P.E., Holberg, D.R.: CMOS Analog Circuit Design. Oxford University Press (2002)

    Google Scholar 

  3. Alpaydin, G., Balkir, S., Dundar, G.: An evolutionary approach to automatic synthesis of high-performance analog integrated circuits. IEEE Trans. Evol. Comput. 7(3), 240–252 (2003)

    Article  Google Scholar 

  4. Altay, E.V., Alatas, B.: Performance comparisons of socially inspired metaheuristic algorithms on unconstrained global optimization. In: Advances in Computer Communication and Computational Sciences, pp. 163–175. Springer (2019)

    Google Scholar 

  5. Andreani, P., Sjoland, H.: Noise optimization of an inductively degenerated cmos low noise amplifier. IEEE Trans. Circuits Syst. II: Analog. Digit. Signal Process. 48(9), 835–841 (2001)

    Article  Google Scholar 

  6. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)

    Google Scholar 

  7. Boyd, S.P., Lee, T.H., et al.: Optimal design of a cmos op-amp via geometric programming. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 20(1), 1–21 (2001)

    Article  Google Scholar 

  8. Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: 2007 IEEE Swarm Intelligence Symposium (SIS 2007), pp. 120–127. IEEE (2007)

    Google Scholar 

  9. Dash, S., Baishnab, K.L., Trivedi, G.: Applying river formation dynamics to analyze vlsi power grid networks. In: 2016 29th International Conference on VLSI Design and 2016 15th International Conference on Embedded Systems (VLSID), pp. 258–263. IEEE (2016)

    Google Scholar 

  10. Dash, S., Joshi, D., Trivedi, G.: Cmos analog circuit optimization via river formation dynamics. In: 2016 26th International Conference Radioelektronika (Radioelektronika), pp. 51–55. IEEE (2016)

    Google Scholar 

  11. De Amorim, R.C., Mirkin, B.: Minkowski metric, feature weighting and anomalous cluster initializing in k-means clustering. Pattern Recognit. 45(3), 1061–1075 (2012)

    Article  Google Scholar 

  12. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  13. Duan, H., Li, S., Shi, Y.: Predator-prey brain storm optimization for dc brushless motor. IEEE Trans. Magn. 49(10), 5336–5340 (2013)

    Article  Google Scholar 

  14. Harjani, R., Rutenbar, R.A., Carley, L.R.: Oasys: a framework for analog circuit synthesis. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 8(12), 1247–1266 (1989)

    Article  Google Scholar 

  15. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2(3), 283–304 (1998)

    Article  Google Scholar 

  16. Lampinen, J.: A constraint handling approach for the differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, 2002, CEC’02, vol. 2, pp. 1468–1473. IEEE (2002)

    Google Scholar 

  17. Lee, C.Y., Yao, X.: Evolutionary programming using mutations based on the lévy probability distribution. IEEE Trans. Evol. Comput. 8(1), 1–13 (2004)

    Article  Google Scholar 

  18. Liu, H., Singhee, A., Rutenbar, R.A., Carley, L.R.: Remembrance of circuits past: macromodeling by data mining in large analog design spaces. In: Proceedings of the 39th Annual Design Automation Conference, pp. 437–442. ACM (2002)

    Google Scholar 

  19. Moore, G.E.: Cramming more components onto integrated circuits, reprinted from Electronics 38(8), April 19, 1965, pp. 114 ff. IEEE Solid-State Circuits Soc. Newsl. 20(3), 33–35 (2006)

    Google Scholar 

  20. Nassif, S.: Power grid analysis benchmarks. In: Asia and South Pacific Design Automation Conference (ASPDAC 2008), pp. 376–381, March 2008

    Google Scholar 

  21. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer Science & Business Media (2006)

    Google Scholar 

  22. Qiu, H., Duan, H., Shi, Y.: A decoupling receding horizon search approach to agent routing and optical sensor tasking based on brain storm optimization. Opt.-Int. J. Light. Electron Opt. 126(7), 690–696 (2015)

    Article  Google Scholar 

  23. Shi, Y.: Brain storm optimization algorithm. In: International Conference in Swarm Intelligence, pp. 303–309. Springer (2011)

    Google Scholar 

  24. Shi, Y.: Brain storm optimization algorithm in objective space. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1227–1234. IEEE (2015)

    Google Scholar 

  25. Shi, Y.: Unified swarm intelligence algorithms. In: Critical Developments and Applications of Swarm Intelligence, pp. 1–26. IGI Global (2018)

    Google Scholar 

  26. Slowik, A., Kwasnicka, H.: Nature inspired methods and their industry applications-swarm intelligence algorithms. IEEE Trans. Ind. Inform. 14(3), 1004–1015 (2018)

    Article  Google Scholar 

  27. Sörensen, K., Sevaux, M., Glover, F.: A history of metaheuristics. In: Handbook of Heuristics, pp. 1–18 (2018)

    Google Scholar 

  28. Sun, C., Duan, H., Shi, Y.: Optimal satellite formation reconfiguration based on closed-loop brain storm optimization. IEEE Comput. Intell. Mag. 8(4), 39–51 (2013)

    Article  Google Scholar 

  29. Tan, S.X.D., Shi, C.J.R., Lee, J.C.: Reliability-constrained area optimization of vlsi power/ground networks via sequence of linear programmings. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 22(12), 1678–1684 (2003)

    Article  Google Scholar 

  30. Tuba, E., Strumberger, I., Zivkovic, D., Bacanin, N., Tuba, M.: Mobile robot path planning by improved brain storm optimization algorithm. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)

    Google Scholar 

  31. Xiong, G., Shi, D.: Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects. Energy (2018)

    Google Scholar 

  32. Xiong, G., Shi, D., Zhang, J., Zhang, Y.: A binary coded brain storm optimization for fault section diagnosis of power systems. Electr. Power Syst. Res. 163, 441–451 (2018)

    Article  Google Scholar 

  33. Yu, T., Wong, M.: Pgt solver: an efficient solver for power grid transient analysis. In: ICCAD (2012)

    Google Scholar 

  34. Zhan, Z.H., Zhang, J., Shi, Y.H., Liu, H.l.: A modified brain storm optimization. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)

    Google Scholar 

  35. Zhou, D., Shi, Y., Cheng, S.: Brain storm optimization algorithm with modified step-size and individual generation. In: Advances in Swarm Intelligence, pp. 243–252 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India

    Satyabrata Dash, Deepak Joshi, Sukanta Dey, Meenali Janveja & Gaurav Trivedi

Authors
  1. Satyabrata Dash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepak Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sukanta Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meenali Janveja
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gaurav Trivedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satyabrata Dash .

Editor information

Editors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Shi Cheng

  2. Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dash, S., Joshi, D., Dey, S., Janveja, M., Trivedi, G. (2019). StormOptimus: A Single Objective Constrained Optimizer Based on Brainstorming Process for VLSI Circuits. In: Cheng, S., Shi, Y. (eds) Brain Storm Optimization Algorithms. Adaptation, Learning, and Optimization, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-15070-9_9

Download citation

Publish with us

Policies and ethics

Search

Navigation