Advertisement

Use of Swarm Intelligence in Structural Steel Design Optimization

  • Mehmet Polat SakaEmail author
  • Serdar Carbas
  • Ibrahim Aydogdu
  • Alper Akin
Chapter
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 7)

Abstract

In this chapter, the optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Corporation). The weight of the steel frame is taken as the objective function to be minimized. The design optimization problem necessitates selection of steel sections for the members of the steel frame from the available steel profiles lists. This turns the design optimization problem into discrete programming problem. Obtaining the optimum solution of such programming problems is cumbersome with mathematical programming techniques. On the other hand with the use of recently developed metaheuristic techniques that are based on swarm intelligence, the solution of the same problem becomes straightforward. Five different structural optimization algorithms are developed which are based on ant colony optimization, particle swarm optimizer, artificial bee colony algorithm, firefly algorithm, and cuckoo search algorithm, respectively. Two real size steel space frames; one rigidly connected and the other pin jointed are designed using each of these algorithms. The optimum designs obtained by these techniques are compared and performance of each version is evaluated. It is noticed that most of swarm intelligence-based algorithms are simple and robust techniques that determine the optimum solution of structural design optimization problems efficiently without requiring much of a mathematical struggle.

Keywords

Structural design optimization Load and resistance factor design (LRFD) Swarm intelligence Ant colony algorithm Particle swam optimizer Artificial bee colony algorithm Firefly algorithm Cuckoo search algorithm 

References

  1. 1.
    Load and Resistance Factor Design (LRFD), Volume 1, Structural Members Specifications Codes, 3rd edn. American Institute of Steel Construction (2001)Google Scholar
  2. 2.
    Rao, S.S.: Engineering Optimization; Theory and Practice, 4th edn. Wiley, New York (2009)Google Scholar
  3. 3.
    Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms, Luniver Press, Bristol (2008)Google Scholar
  4. 4.
    Kochenberger, G.A., Glover, F.: Handbook of Meta-Heuristics. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  5. 5.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(30), 268–308 (2003)CrossRefGoogle Scholar
  6. 6.
    De Castro, L.N., Von Zuben, F.J.: Recent Developments in Biologically Inspired Computing. Idea Group Publishing, Hershey (2005)Google Scholar
  7. 7.
    Dreo, J., Petrowski, A., Siarry, P., Taillard, E.: Meta-Heuristics for Hard Optimization. Springer, Berlin (2006)zbMATHGoogle Scholar
  8. 8.
    Gonzales, T.F.: Handbook of Approximation Algorithms and Metaheuristics. Chapman&Hall, CRC Press, London (2007)Google Scholar
  9. 9.
    Luke, S.: Essentials of Metaheuristics. http://cs.gmu.edu/~sean/book/metaheuristics/ (2010). Accessed May 2015
  10. 10.
    Saka, M.P., Dogan, E., Aydogdu, I.: Review and analysis of swarm-intelligence based algorithms, Chapter 2. In: Yang, Cui, Xiao, Gandomi (eds.) Swarm Intelligence and Bio-Inspired Computation, Theory and Applications. Elsevier, Amsterdam. ISBN: 978-0-12-405163-8 (2013)Google Scholar
  11. 11.
    Saka, M.P.: Optimum design of skeletal structures: a review, Chapter 10. In: Topping, B.H.V. (ed.) Progress in Civil and Structural Engineering Computing, pp. 237–284. Saxe-Coburg Publications, Edinburgh (2003)Google Scholar
  12. 12.
    Saka, M.P.: Optimum design of steel frames using stochastic search techniques based on natural phenomena: a review, Chapter 6. In: Topping, B.H.V. (ed.) Civil Engineering Computations: Tools and Techniques, pp. 105–147. Saxe-Coburgh Publications, Edinburgh (2007)Google Scholar
  13. 13.
    Lamberti, L., Pappalettere, C.: Metaheuristic design optimization of skeletal structures: a review. Comput. Technol. Rev. 4, 1–32 (2011)CrossRefGoogle Scholar
  14. 14.
    Saka, M.P.: Recent developments in metaheuristic algorithms: a review. Comput. Technol. Rev. 5, 31–78 (2012)CrossRefGoogle Scholar
  15. 15.
    Saka, M.P., Geem, Z.W.: Mathematical and metaheuristic applications in design optimization of steel frame structures: an extensive review. Math. Probl. Eng. (2013)Google Scholar
  16. 16.
    Saka, M.P.: Shape and topology optimization design of skeletal structures using metaheuristic algorithms: a review. Comput. Technol. Rev. 9, 31–68 (2014)CrossRefGoogle Scholar
  17. 17.
    Ad Hoc Committee on Serviceability: Structural serviceability: a critical appraisal and research needs. J. Struct. Eng. ASCE 112(12), 2646–2664 (1986)CrossRefGoogle Scholar
  18. 18.
    Ellingwood, B.: Serviceability guidelines for steel structures. Eng. J. AISC 26, 1–8 (1989)Google Scholar
  19. 19.
    Chen, W.F., Kim, S.-E.: LRFD Steel Design Using Advanced Analysis. CRC Press, Boca Raton (1997)Google Scholar
  20. 20.
    ASCE 7-05: Minimum Design Loads for Building and Other Structures (2005)Google Scholar
  21. 21.
    Hasançebi, O., Çarbaş, S., Doğan, E., Erdal, F., Saka, M.P.: Performance evaluation of metaheuristic techniques in the optimum design of real size pin jointed structures. Comput. Struct. 87(5–6), 284–302 (2009)CrossRefGoogle Scholar
  22. 22.
    Hasançebi, O., Çarbaş, S., Doğan, E., Erdal, F., Saka, M.P.: Comparison of non-deterministic search techniques in the optimum design of real size steel frames. Comput. Struct. 88(17–18), 1033–1048 (2010)CrossRefGoogle Scholar
  23. 23.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by Ant Colony. In: Proceedings of First European Conference on Artificial Life, U.S.A., pp. 134–142 (1991)Google Scholar
  24. 24.
    Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Dipartimento Elettronica e Informazione, Politecnico di Milano, Italy (1992)Google Scholar
  25. 25.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Book, Massachusetts Institute of Technology, U.S.A. (2004)zbMATHGoogle Scholar
  26. 26.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE Press (1995)Google Scholar
  27. 27.
    Kennedy, J., Eberhart, R., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  28. 28.
    Karaboga, D.: An idea based on Honey Bee Swarm for numerical optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  29. 29.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) Algorithm. J. Global Optim. 39(3), 459–471 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Yang, X.-S.: Firefly algorithms for multimodal optimization, chapter in Stochastic algorithms: foundations and applications. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009, Lecture Notes in Computer Science, vol. 5792, pp. 169–178 (2009)Google Scholar
  31. 31.
    Yang, X.-S., He, X.: Firefly algorithm: recent advances and applications. Int. J. Swarm Intell. 1(1), 36–50 (2013)CrossRefGoogle Scholar
  32. 32.
    Gandomi, A.H., Yang, X.-S., Alavi, A.H.: Mixed variable structural optimization using Firefly algorithm. Comput. Struct. 89(23–24), 2325–2336 (2011)CrossRefGoogle Scholar
  33. 33.
    Yang, X.-S., Deb, S.: Engineering optimization by Cuckoo Search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)zbMATHGoogle Scholar
  34. 34.
    Coello, C.A.C.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191, 1245–1287 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Carbas, S., Saka, M.P.: Efficiency of improved harmony search algorithm for solving engineering optimization problems. Int. J. Optim. Civil Eng. 3(1), 99–114 (2013)Google Scholar
  36. 36.
    Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys. Rev. 49(5), 4677–4683 (1994)Google Scholar
  37. 37.
    ASCE 7-98: Minimum Design Loads for Buildings and Other Structures, American Society of Civil Engineers (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mehmet Polat Saka
    • 1
    Email author
  • Serdar Carbas
    • 2
  • Ibrahim Aydogdu
    • 3
  • Alper Akin
    • 4
  1. 1.Department of Civil EngineeringUniversity of BahrainIsa TownBahrain
  2. 2.Department of Civil EngineeringKaramanoglu Mehmetbey UniversityKaramanTurkey
  3. 3.Department of Civil EngineeringAkdeniz UniversityAntalyaTurkey
  4. 4.Thomas & Betts Corporation, Meyer Steel StructuresMemphisUSA

Personalised recommendations