Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Solving design optimization problems via hunting search algorithm with Levy flights

  • Dogan, Erkan (Department of Civil Engineering, Faculty of Engineering, Celal Bayar University)
  • Received : 2014.02.12
  • Accepted : 2014.06.29
  • Published : 2014.10.25
⟨ Previous Next ⟩

Abstract

This study presents a hunting search based optimum design algorithm for engineering optimization problems. Hunting search algorithm is an optimum design method inspired by group hunting of animals such as wolves, lions, and dolphins. Each of these hunters employs hunting in a different way. However, they are common in that all of them search for a prey in a group. Hunters encircle the prey and the ring of siege is tightened gradually until it is caught. Hunting search algorithm is employed for the automation of optimum design process, during which the design variables are selected for the minimum objective function value controlled by the design restrictions. Three different examples, namely welded beam, cellular beam and moment resisting steel frame are selected as numerical design problems and solved for the optimum solution. Each example differs in the following ways: Unlike welded beam design problem having continuous design variables, steel frame and cellular beam design problems include discrete design variables. Moreover, while the cellular beam is designed under the provisions of BS 5960, LRFD-AISC (Load and Resistant Factor Design-American Institute of Steel Construction) is considered for the formulation of moment resisting steel frame. Levy Flights is adapted to the simple hunting search algorithm for better search. For comparison, same design examples are also solved by using some other well-known search methods in the literature. Results reveal that hunting search shows good performance in finding optimum solutions for each design problem.

Keywords

References

  1. Adami, C. (1998), An introduction to Artificial Life, Springer Verlag Telos, London, UK.
  2. Arora, J.S. (2002), Methods for Discrete Variable Structural Optimization, Recent Advances in Optimum Structural Design, Ed. Burns, S.A., ASCE, USA.
  3. Bonabeau, E., Dorigo, M. and Theraulaz, G. (1999), Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, U.K.
  4. British Standards BS 5950 (2000), Structural Use of Steelworks in Building. Part 1. Code of Practice for Design in Simple and Continuous construction, hot rolled sections, British Standard Institution, London, U.K.
  5. Camp, C.V., Bichon, J.B. and Stovall, S.P. (2004), "Design of steel frames using ant colony optimization", J. Struct. Eng., ASCE, 131(3), 369-79.
  6. Camp, C.V. (2007), "Design of Space Trusses Using Big Bang-Big Crunch Optimization", J. Struct. Eng., ASCE, 133(7), 999-1008. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:7(999)
  7. Coello, C.A.C. (2002), "Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art", Comput. Meth. Appl. Mech. Eng., 191, 1245-1287. https://doi.org/10.1016/S0045-7825(01)00323-1
  8. Dionisio, M.C., Hoffman, R.M., Yost, J.R., Dinehart, D.W. and Gross, S.P. (2004), "Determination of critical location for service load bending stresses in non-composite cellular beams", 17th ASCE Engineering Mechanics Conference, University of Delaware, Newark, DE.
  9. Deb, K. (1991), "Optimal design of a welded beam via genetic algorithms", Am. Ins. Aeronaut. Astronaut. J., 29, 2013-2015. https://doi.org/10.2514/3.10834
  10. Deb, K. (2000), "An efficient constraint handling method for genetic algorithms", Comput. Meth. Appl. Mech. Eng., 186(2-4), 311-338. https://doi.org/10.1016/S0045-7825(99)00389-8
  11. Dogan, E. and Saka, M.P. (2012), "Optimum design of unbraced steel frames to LRFD-AISC using particle swarm optimization", Adv. Eng. Softw., 46(1), 27-34. https://doi.org/10.1016/j.advengsoft.2011.05.008
  12. Duran, O., Perez, L. and Batoccia, A (2012). "Optimization of modular structures using particle swarm optimization", Exp. Syst. Appl., 39(3), 3507-3515. https://doi.org/10.1016/j.eswa.2011.09.041
  13. Erdal, F., Dogan, E. and Saka, M.P. (2011), "Optimum design of cellular beams using harmony search and particle swarm optimizers", J. Construct. Steel Res., 67(2), 237-247. https://doi.org/10.1016/j.jcsr.2010.07.014
  14. Erdal, F., Dogan, E. and Saka, M.P. (2013), "An improved particle swarm optimizer for steel grillage systems", Struct. Eng. Mech., 47(4), 513-530. https://doi.org/10.12989/sem.2013.47.4.513
  15. Erol, O. and Eksin, I. (2006), "A new optimization method: big bang-big crunch", Adv. Eng. Softw., 37, 106-111. https://doi.org/10.1016/j.advengsoft.2005.04.005
  16. Fioriti, M. (2014), "Adaptable conceptual aircraft design model", Adv. Aircraf. Spacecraft Sci., , 1(1), 43-67. https://doi.org/10.12989/aas.2014.1.1.043
  17. Hasancebi, O. (2008), "Adaptive evolution strategies in structural optimization: enhancing their computational performance with applications to large-scale structures", Comput. Struct., 86, 119-132. https://doi.org/10.1016/j.compstruc.2007.05.012
  18. He, S., Prempain, E. and Wu, Q.H. (2004), "An improved particle swarm optimizer for mechanical design optimization problems", Eng. Optim., 36(5), 585-605. https://doi.org/10.1080/03052150410001704854
  19. Karaboga, D. (2010), "Artificial bee colony algorithm", Scholarpedia, 5(3), 6915.
  20. Keane, A.J. (1995), "A brief comparison of some evolutionary optimization methods", Proceedings of the conference on applied decision technologies (modern heuristic search methods), Uxbrigde: Wiley.
  21. Kennedy, J. and Eberhart, R.C. (1995), "Particle swarm optimization", Proceedings of IEEE International Conference on Neural Networks, NJ, Piscataway.
  22. Knowles, P.R. (1985), Design of Castellated beams, The Steel Construction Institute.
  23. Kochenberger, G.A. (2003), Handbook of meta-heuristics, Kluwer Academic Publishers, Norwell, USA.
  24. Lawson, R.M., Lim, J., Hicks, S.J. and Simms, W.I. (2006), "Design of composite asymmetric cellular beams and beams with large openings", J. Construct. Steel Res., 62, 614-629. https://doi.org/10.1016/j.jcsr.2005.09.012
  25. LRFD-AISC (1999), Manual of steel construction, Load and resistance factor design. Metric conversion of the second edition, Vol. I & II. AISC, USA.
  26. Mantegna, R.N. (1994), "Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes", Phys. Rev. E, 49(5), 4677-4683. https://doi.org/10.1103/PhysRevE.49.4677
  27. McGuire, W. (1968), Steel structures, Prentice-Hall, Englewood, USA.
  28. Oftadeh, R., Mahjoob, M.J. and Shariatpanahi, M. (2010), "A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search", Comput. Math. Appl., 60, 2087-2098. https://doi.org/10.1016/j.camwa.2010.07.049
  29. Ragsdell, K.M. and Phillips, D.T. (1976), "Optimal design of a class of welded structures using geometric programming", Am. Soc. Mech. Eng. J. Eng. Indus., 98, 1021-1025.
  30. Saka, M.P. and Dogan, E. (2012), "Design optimization of moment resisting steel frames using a cuckoo search algorithm", Proceedings of The Eleventh International Conference on Computational Structures Technology, Dubrovnik, Croatia, September.
  31. Yang, X.S. (2009), Firefly Algorithms for Multimodal Optimization. Stochastic Algorithms: Foundations and Applications, SAGA, Lecture Notes in Computer Science, Cambridge, UK.
  32. Yang, X.S. and Deb, S. (2010), "Engineering optimization by cuckoo search", Int. J. Math. Model. Numer. Opt., 1(4), 330-343.
  33. Ward, J.K. (1990), Design of composite and non-composite cellular beams, The Steel Construction Institute Publication, Steel Construction Institute, Ascot, UK.

Cited by

  1. Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis vol.10, pp.11, 2015, https://doi.org/10.17706/jsw.10.11.1301-1309
  2. Optimal design of double layer barrel vaults considering nonlinear behavior vol.58, pp.6, 2016, https://doi.org/10.12989/sem.2016.58.6.1109
  3. Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow vol.30, pp.12, 2016, https://doi.org/10.1007/s11269-016-1424-5
  4. Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm vol.8, pp.6, 2016, https://doi.org/10.7763/IJCTE.2016.V8.1096