Performance Comparison of Metaheuristic Algorithms for the Optimal Design of Space Trusses

Research Article - Civil Engineering
  • 30 Downloads

Abstract

In this study, eight population-based metaheuristic algorithms were employed for the design of truss structures with continuous design variables. The selected algorithms were genetic, ant colony, particle swarm, artificial bee colony, gravitational search, firefly, gray wolf optimization and Jaya. The purpose was to objectively evaluate the performance of these algorithms under the same conditions and select the best efficient algorithm by assessing three example truss structures. The results obtained from the examples showed that the algorithms were both computationally efficient and robust when the number of design variables was approximately 10 and a significant number of iterations were performed. When the number of design variables was increased to 53, artificial bee colony, Jaya and gray wolf optimization were found to be computationally more effective than the remaining algorithms.

Keywords

Genetic algorithm Ant colony optimization Artificial bee colony Gray wolf optimization Firefly algorithms Gravitational search algorithm Jaya 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Saka, M.P.; Hasançebi, O.; Geem, Z.W.: Metaheuristics in structural optimization and discussions on harmony search algorithm. Swarm Evol. Comput. 28, 88–97 (2015).  https://doi.org/10.1016/j.swevo.2016.01.005 CrossRefGoogle Scholar
  2. 2.
    Hare, W.; Nutini, J.; Tesfamariam, S.: A survey of non-gradient optimization methods in structural engineering. Adv. Eng. Softw. 59, 19–28 (2013).  https://doi.org/10.1016/j.advengsoft.2013.03.001 CrossRefGoogle Scholar
  3. 3.
    Kaveh, A.: Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, Berlin (2014)CrossRefMATHGoogle Scholar
  4. 4.
    Colorni, A.; Dorigo, M.; Maniezzo, V.: An investigation of some properties of an “Ant Algorithm”. In: PPSN 2–7 (1992)Google Scholar
  5. 5.
    Kennedy, J.; Eberhart, R.: Particle swarm optimization. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=488968 (1995)
  6. 6.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes Univ. 10 (2005). citeulike-article-id:6592152Google Scholar
  7. 7.
    Pham, D.T.; Ghanbarzadeh, A.; Koç, E.; Otri, S.; Rahim, S.; Zaidi, M.: The bees algorithm—a novel tool for complex optimisation problems. In: 2nd IPROMS Virtual International Conference on Intell. Prod. Mach. Syst., 3–14 July 2006, pp. 454–459 (2006)Google Scholar
  8. 8.
    X.S., Y.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Frome (2010).Google Scholar
  9. 9.
    Rao, R.V.; Savsani, V.J.; Vakharia, D.P.: Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf. Sci. (Ny) 183, 1–15 (2012).  https://doi.org/10.1016/j.ins.2011.08.006 MathSciNetCrossRefGoogle Scholar
  10. 10.
    Mirjalili, S.; Mirjalili, S.M.; Lewis, A.: Grey Wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014).  https://doi.org/10.1016/j.advengsoft.2013.12.007 CrossRefGoogle Scholar
  11. 11.
    Bingol, H.; Alatas, B.: Chaotic league championship algorithms. Arab. J. Sci. Eng. 41, 5123–5147 (2016).  https://doi.org/10.1007/s13369-016-2200-9 MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kaveh, A.; Farhoudi, N.: A new optimization method: Dolphin echolocation. Adv. Eng. Softw. 59, 53–70 (2013).  https://doi.org/10.1016/j.advengsoft.2013.03.004 CrossRefGoogle Scholar
  13. 13.
    Melanie, M.: An Introduction to Genetic Algorithms Library of Congress Cataloging-in-Publication Data. MIT Press, Boston (1998)Google Scholar
  14. 14.
    Storn, R.; Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997).  https://doi.org/10.1023/A:1008202821328 MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Lee, K.S.; Geem, Z.W.: A new structural optimization method based on the harmony search algorithm. Comput. Struct. 82, 781–798 (2004).  https://doi.org/10.1016/j.compstruc.2004.01.002 CrossRefGoogle Scholar
  16. 16.
    Kaveh, A.; Talatahari, S.: An improved ant colony optimization for constrained engineering design problems. Eng. Comput. 27, 155–182 (2010).  https://doi.org/10.1108/02644401011008577 CrossRefMATHGoogle Scholar
  17. 17.
    Erol, O.K.; Eksin, I.: A new optimization method: big bang–big crunch. Adv. Eng. Softw. 37, 106–111 (2006).  https://doi.org/10.1016/j.advengsoft.2005.04.005 CrossRefGoogle Scholar
  18. 18.
    Rashedi, E.; Nezamabadi-pour, H.; Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. (Ny) 179, 2232–2248 (2009).  https://doi.org/10.1016/j.ins.2009.03.004 CrossRefMATHGoogle Scholar
  19. 19.
    Eskandar, H.; Sadollah, A.; Bahreininejad, A.; Hamdi, M.: Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 110–111, 151–166 (2012).  https://doi.org/10.1016/j.compstruc.2012.07.010 CrossRefGoogle Scholar
  20. 20.
    Kaveh, A.; Bakhshpoori, T.: Water evaporation optimization: a novel physically inspired optimization algorithm. Comput. Struct. 167, 69–85 (2016).  https://doi.org/10.1016/j.compstruc.2016.01.008 CrossRefGoogle Scholar
  21. 21.
    Kaveh, A.; Khayatazad, M.: A new meta-heuristic method: ray optimization. Comput. Struct. 112–113, 283–294 (2012).  https://doi.org/10.1016/j.compstruc.2012.09.003 CrossRefGoogle Scholar
  22. 22.
    Kaveh, A.; Mahdavi, V.R.: Colliding bodies optimization: a novel meta-heuristic method. Comput. Struct. 139, 18–27 (2014).  https://doi.org/10.1016/j.compstruc.2014.04.005 CrossRefGoogle Scholar
  23. 23.
    Jaya Rao, R.V.: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19–34 (2016).  https://doi.org/10.5267/j.ijiec.2015.8.004 Google Scholar
  24. 24.
    Jalkanen, J.; Koski, J.: Heuristic methods in space frame optimization. In: 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, pp. 1–15. AIAA 2005-190, Austin (2005)Google Scholar
  25. 25.
    Hasançebi, O.; Çarbaş, S.; Dogan, E.; Erdal, F.; Saka, M.P.: Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Comput. Struct. 87, 284–302 (2009).  https://doi.org/10.1016/j.compstruc.2009.01.002 CrossRefGoogle Scholar
  26. 26.
    Kaveh, A.; Zolghadr, A.: Comparison of nine meta-heuristic algorithms for optimal design of truss structures with frequency constraints. Adv. Eng. Softw. 76, 9–30 (2014).  https://doi.org/10.1016/j.advengsoft.2014.05.012 CrossRefGoogle Scholar
  27. 27.
    Stolpe, M.: Truss optimization with discrete design variables: a critical review. Struct. Multidiscipl. Optim. 53, 349–374 (2016).  https://doi.org/10.1007/s00158-015-1333-x MathSciNetCrossRefGoogle Scholar
  28. 28.
    AISC: Specification for Structural Steel Buildings. Allowable Stress Design (ASD), 9th edn. American Institute of Steel Construction, Inc., Chicago, IL (1989)Google Scholar
  29. 29.
    Kaveh, A.; Hassani, B.; Shojaee, S.; Tavakkoli, S.M.: Structural topology optimization using ant colony methodology. Eng. Struct. 30, 2559–2565 (2008).  https://doi.org/10.1016/j.engstruct.2008.02.012 CrossRefGoogle Scholar
  30. 30.
    Kameshki, E.S.; Saka, M.P.: Genetic algorithm based optimum bracing design of non-swaying tall plane frames. J. Constr. Steel Res. 57, 1081–1097 (2001).  https://doi.org/10.1016/S0143-974X(01)00017-7 CrossRefGoogle Scholar
  31. 31.
    Deb, K.; Kumar, A.: Real-coded genetic algorithms with simulated binary crossover: studies on multimodal and multiobjective problems. Complex Syst. 9, 431–454 (1995)Google Scholar
  32. 32.
    Rahami, H.; Kaveh, A.; Gholipour, Y.: Sizing, geometry and topology optimization of trusses via force method and genetic algorithm. Eng. Struct. 30, 2360–2369 (2008).  https://doi.org/10.1016/j.engstruct.2008.01.012 CrossRefGoogle Scholar
  33. 33.
    Dorigo, M.; Stützle, T.: Ant Colony Optimization. The MIT Press, Cambridge, Massachusetts (2004)MATHGoogle Scholar
  34. 34.
    Socha, K.; Dorigo, M.: Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185, 1155–1173 (2008).  https://doi.org/10.1016/j.ejor.2006.06.046 MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    Kennedy, J.; Eberhart, R.C.; Shi, Y.: Swarm Intelligence. Academic Press, San Francisco, CA (2001)Google Scholar
  36. 36.
    Khalifa, A.E.; Imteyaz, B.A.; Lawal, D.U.; Abido, M.A.: Heuristic optimization techniques for air gap membrane distillation system. Arab. J. Sci. Eng. 42, 1951–1965 (2017).  https://doi.org/10.1007/s13369-016-2391-0 CrossRefGoogle Scholar
  37. 37.
    Karaboga, D.: ABC Homepage. http://mf.erciyes.edu.tr/abc/
  38. 38.
    Fister, I.; Yang, X.S.; Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013).  https://doi.org/10.1016/j.swevo.2013.06.001 CrossRefGoogle Scholar
  39. 39.
    Rao, R.V.; Saroj, A.: A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evolut. Comput. (2017).  https://doi.org/10.1016/j.swevo.2017.04.008 Google Scholar
  40. 40.
    Rao, R.V.; More, K.C.; Taler, J.; Ocłoń, P.: Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl. Therm. Eng. 103, 572–582 (2016).  https://doi.org/10.1016/j.applthermaleng.2016.04.135 CrossRefGoogle Scholar
  41. 41.
    MatLab Release (2015) The MathWorks Inc., Natick, MA, USAGoogle Scholar
  42. 42.
    Sonmez, M.: Discrete optimum design of truss structures using artificial bee colony algorithm. Struct. Multidiscipl. Optim. 43, 85–97 (2010).  https://doi.org/10.1007/s00158-010-0551-5 CrossRefGoogle Scholar
  43. 43.
    ASCE: Minimum Design Loads for Buildings and Other Structures. American Society of Civil Engineers, Reston (2005)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Department of Civil Engineering, Engineering FacultyAksaray UniversityAksarayTurkey

Personalised recommendations