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A Modified Big Bang–Big Crunch Algorithm for Structural Topology Optimization

  • Hong-Kyun Ahn
  • Dong-Seok Han
  • Seog-Young HanEmail author
Regular Paper
  • 88 Downloads

Abstract

The purpose of this study is to develop a topology optimization scheme based on big bang–big crunch (BB–BC) algorithm, inspired from the evolution of the universe called big bang and big crunch theory. In order to apply the BB–BC algorithm to topology optimization for static and dynamic stiffness problems, the parameters of the algorithm were transformed to those of topology optimization scheme. In addition, some parameters such as big bang (BB) range, BB search, population and non-exchange limit were newly introduced to topology optimization scheme. Also, a parametric study for the parameters involved in the topology optimization scheme was performed to reduce the number of parameters, and find the appropriate ranges for topology optimization. Some examples were provided to examine the effectiveness of the developed topology optimization scheme for both static and dynamic stiffness problems throughout comparing with other metaheuristic topology optimization algorithms and the BESO (bi-directional evolutionary structural optimization) method. It was verified that the suggested algorithm shows superior to the compared typical metaheuristic topology optimization algorithms in the viewpoints of stability, robustness, accuracy and the convergence rate.

Keywords

Big bang–big crunch (BB–BC) algorithm Metaheuristic method Topology optimization Static stiffness problem Dynamic stiffness problem 

Notes

References

  1. 1.
    Erol, O. K., & Eksin, I. (2006). A new optimization method: Big bang–big crunch. Advances in Engineering Software,37(2), 106–111.CrossRefGoogle Scholar
  2. 2.
    The big bang and the big crunch. https://www.physicsoftheuniverse.com/topics_bigbang.html. Accessed 10 Feb 2018.
  3. 3.
    Big crunch. https://en.wikipedia.org/wiki/Big_Crunch. Accessed 10 Feb 2018.
  4. 4.
    Jordehi, A. R. (2014). A chaotic-based big bang–big crunch algorithm for solving global optimisation problems. Neural Computing and Applications,25(6), 1329–1335.CrossRefGoogle Scholar
  5. 5.
    Alatas, B. (2011). Uniform big bang–chaotic big crunch optimization. Communications in Nonlinear Science and Numerical Simulation,16(9), 3696–3703.CrossRefGoogle Scholar
  6. 6.
    Jaradat, G. M., & Ayob, M. (2010). Big bang–big crunch optimization algorithm to solve the course timetabling problem. In: 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 1448–1452).Google Scholar
  7. 7.
    Hatamlou, A., Abdullah, S., & Hatamlou, M. (2011). Innovative computing technology. In P. Pichappan, H. Ahmadi, & E. Ariwa (Eds.), Data clustering using big bang–big crunch algorithm (pp. 383–388). New York: Springer.Google Scholar
  8. 8.
    Kaveh, A., & Talatahari, S. (2009). Size optimization of space trusses using big bang–big crunch algorithm. Computers & Structures,87(17–18), 1129–1140.CrossRefGoogle Scholar
  9. 9.
    Camp, C. V. (2007). Design of space trusses using big bang–big crunch optimization. Journal of Structural Engineering,133(7), 999–1008.CrossRefGoogle Scholar
  10. 10.
    Kaveh, A., & Talatahari, S. (2010). Optimal design of Schwedler and ribbed domes via hybrid big bang–big crunch algorithm. Journal of Constructional Steel Research,66(3), 412–419.CrossRefGoogle Scholar
  11. 11.
    Kaveh, A., & Mahdavi, V. R. (2013). Optimal design of structures with multiple natural frequency constraints using a hybridized BB–BC/Quasi-Newton Algorithm. Periodica Polytechnica Civil Engineering,57(1), 27–38.CrossRefGoogle Scholar
  12. 12.
    Bendsoe, M. P., & Sigmund, O. (2003). Topology optimization: Theory, methods and applications. Topology optimization by distribution of isotropic material (pp. 1–69). Berlin: Springer.Google Scholar
  13. 13.
    Huang, X., & Xie, M. (2010). Evolutionary topology optimization of continuum structures: Methods and applications. Bi-directional evolutionary structural optimization method (pp. 17–38). Chichester: Wiley.Google Scholar
  14. 14.
    Saka, M. P., Hasançebi, O., & Geem, Z. W. (2016). Metaheuristics: In structural optimization and discussions on harmony search algorithm. Swarm and Evolutionary Computation,28, 88–97.CrossRefGoogle Scholar
  15. 15.
    Sorensen, K. (2015). Metaheuristics—The metaphor exposed. International Transactions in Operational Research,22(1), 3–18.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Park, J. Y., & Han, S. Y. (2013). Swarm intelligence topology optimization based on artificial bee colony algorithm. International Journal of Precision Engineering and Manufacturing,14(1), 115–121.CrossRefGoogle Scholar
  17. 17.
    Park, J. Y., & Han, S. Y. (2013). Application of artificial bee colony algorithm to topology optimization for dynamic stiffness problems. Computers & Mathematics with Applications,66(10), 1879–1891.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Lee, S. M., & Han, S. Y. (2017). Topology optimization based on the harmony search method. Journal of Mechanical Science and Technology,31(6), 2875–2882.CrossRefGoogle Scholar
  19. 19.
    Lee, S. M., & Han, S. Y. (2016). Topology optimization scheme for dynamic stiffness problems using harmony search method. International Journal of Precision Engineering and Manufacturing,17(9), 1187–1194.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringHanyang UniversitySeoulSouth Korea

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