Application of the Flower Pollination Algorithm in Structural Engineering

  • Sinan Melih Nigdeli
  • Gebrail Bekdaş
  • Xin-She YangEmail author
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 7)


In the design of a structural system, the optimum values of design variables cannot be derived analytically. Structural engineering problems have various design constraints concerning structural security measures and practicability in production. Thus, optimization becomes an important part of the design process. Recent studies suggested that metaheuristic methods using random search procedures are effective for solving optimization problems in structural engineering. In this chapter, the flower pollination algorithm (FPA) is presented for dealing with structural engineering problems. The engineering problems are about pin-jointed plane frames, truss systems, deflection minimization of I-beams, tubular columns, and cantilever beams. The FPA inspired from the reproduction of flowers via pollination is effective to find the best optimum results when compared to other methods. In addition, the computing time is usually shorter and the optimum results are also robust.


Metaheuristic methods Flower pollination algorithm Structural optimization Topology optimization Weight optimization 


  1. 1.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)Google Scholar
  2. 2.
    Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, New York (2010)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Boston (1989)zbMATHGoogle Scholar
  4. 4.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  5. 5.
    Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26, 29–41 (1996)CrossRefGoogle Scholar
  7. 7.
    Nakrani, S., Tovey, C.: On honey bees and dynamic allocation in an internet server colony. Adapt. Behav. 12(3–4), 223–240 (2004)CrossRefGoogle Scholar
  8. 8.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks No. IV, 27 Nov–1 Dec, pp. 1942–1948, Perth Australia (1995)Google Scholar
  9. 9.
    Glover, F.: Heuristic for integer programming using surrogate constraints. Decis. Sci. 8, 156–166 (1977)CrossRefGoogle Scholar
  10. 10.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76, 60–68 (2001)CrossRefGoogle Scholar
  11. 11.
    Erol, O.K., Eksin, I.: A new optimization method: big bang big crunch. Adv. Eng. Softw. 37, 106–111 (2006)CrossRefGoogle Scholar
  12. 12.
    Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 17–35 (2013)CrossRefGoogle Scholar
  13. 13.
    Kaveh, A., Talatahari, A.: A novel heuristic optimization method: charged system search. Acta Mech. 213, 267–289 (2010)CrossRefzbMATHGoogle Scholar
  14. 14.
    Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)CrossRefGoogle Scholar
  15. 15.
    Yang, X.S., Deb, S.: Two-stage eagle strategy with differential evolution. Int. J. Bio-Inspired Comput. 4(1), 1–5 (2012)CrossRefGoogle Scholar
  16. 16.
    Yang, X.S.: Flower pollination algorithm for global optimization. In: Unconventional Computation and Natural Computation 2012. Lecture Notes in Computer Science, vol. 7445, pp. 240–249 (2012)Google Scholar
  17. 17.
    Kaveh, A., Khayatazad, M.: A novel meta-heuristic method: ray optimization. Comput. Struct. 112–113, 283–294 (2012)CrossRefGoogle Scholar
  18. 18.
    Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optim. 46(9), 1222–1237 (2012)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Majid, K.I.: Optimum design of structures. Newnes-Butterworth, London (1974)Google Scholar
  20. 20.
    Li, J.P., Balazs, M.E., Parks, G.T.: Engineering design optimization using species-conserving genetic algorithms. Eng. Optm. 39(2), 147–161 (2007)CrossRefGoogle Scholar
  21. 21.
    Nowcki, H.: Optimization in pre-contract ship design. In: Fujita, Y., Lind, K., Williams, T.J. (eds.) Computer Applications in the Automation of Shipyard Operation and Ship Design, vol. 2, pp. 327–338. Elsevier, New York (1974)Google Scholar
  22. 22.
    Park, Y.C., Chang, M.H., Lee, T.Y.: A new deterministic global optimization method for general twice differentiable constrained nonlinear programming problems. Eng. Optim. 39(4), 397–411 (2007)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Ray, T., Saini, P.: Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng. Optm. 33(6), 735–748 (2001)CrossRefGoogle Scholar
  24. 24.
    Tsai, J.: Global optimization of nonlinear fractional programming problems in engineering design. Eng. Optim. 37(4), 399–409 (2005)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Gold, S., Krishnamurty, S.: Trade-offs in robust engineering design. In: Proceedings of the 1997 ASME Design Engineering Technical Conferences, DETC97/DAC3757, 14–17 Sept, Saramento, California (1997)Google Scholar
  26. 26.
    Wang, G.G.: Adaptive response surface method using inherited latin hypercube design points. Trans. ASME 125, 210–220 (2003)CrossRefGoogle Scholar
  27. 27.
    Hsu, Y.L., Liu, T.C.: Developing a fuzzy proportionalderivative controller optimization engine for engineering design optimization problems. Eng. Optm. 39(6), 679–700 (2007)CrossRefGoogle Scholar
  28. 28.
    Rao, S.S.: Engineering optimization: theory and practice, 3rd edn. Wiley, Chichester (1996)Google Scholar
  29. 29.
    Fleury, C., Braibant, V.: Structural optimization: a new dual method using mixed variables. Int. J. Numer. Meth. Eng. 23, 409–428 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Chickermane, H., Gea, H.C.: Structural optimization using a new local approximation method. Int. J. Numer. Meth. Eng. 39, 829–846 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Thanedar, P.B., Vanderplaats, G.N.: Survey of discrete variable optimization for structural design. J. Struct. Eng. ASCE 121(2), 301–306 (1995)Google Scholar
  32. 32.
    Lamberti, L., Pappalettere, C.: Move limits definition in structural optimization with sequential linear programming. Part II Numer. Ex. Comput. Struct. 81, 215–238 (2003)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Huang, M.W., Arora, J.S.: Optimal design with discrete variables: some numerical experiments. Int. J. Numer. Meth. Eng. 40, 165–188 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sinan Melih Nigdeli
    • 1
  • Gebrail Bekdaş
    • 1
  • Xin-She Yang
    • 2
    Email author
  1. 1.Department of Civil EngineeringIstanbul UniversityAvcılarTurkey
  2. 2.Design Engineering and MathematicsMiddlesex University LondonLondonUK

Personalised recommendations