Steel and Composite Structures

  • 제45권4호
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  • Pages.579-590
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  • 2022
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Simulated squirrel search algorithm: A hybrid metaheuristic method and its application to steel space truss optimization

  • Pauletto, Mateus P. (Departament of Civil Engineering, University of Passo Fundo Campus 1) ;
  • Kripka, Moacir (Departament of Civil Engineering, University of Passo Fundo Campus 1)
  • 투고 : 2022.01.27
  • 심사 : 2022.11.14
  • 발행 : 2022.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

One of the biggest problems in structural steel calculation is the design of structures using the lowest possible material weight, making this a slow and costly process. To achieve this objective, several optimization methods have been developed and tested. Nevertheless, a method that performs very efficiently when applied to different problems is not yet available. Based on this assumption, this work proposes a hybrid metaheuristic algorithm for geometric and dimensional optimization of space trusses, called Simulated Squirrel Search Algorithm, which consists of an association of the well-established neighborhood shifting algorithm (Simulated Annealing) with a recently developed promising population algorithm (Squirrel Search Algorithm, or SSA). In this study, two models are tried, being respectively, a classical model from the literature (25-bar space truss) and a roof system composed of space trusses. The structures are subjected to resistance and displacement constraints. A penalty function using Fuzzy Logic (FL) is investigated. Comparative analyses are performed between the Squirrel Search Algorithm (SSSA) and other optimization methods present in the literature. The results obtained indicate that the proposed method can be competitive with other heuristics.

키워드

과제정보

The first author is grateful to CAPES/Brazil for the support. The second author is grateful for the financial support received from the Brazilian government in the form of a CNPq grant.

참고문헌

  1. Ahmadvand, H. and Habibi, A. (2020), "Optimum design of shape and size of truss structures via a new approximation method", Struct. Eng. Mech., 76(6), 799-821. https://doi.org/10.12989/sem.2020.76.6.799.
  2. NBR 8800 (2008), Projeto de estruturas de aco e de estruturas mistas de aco e concreto de edificios, Associacao Brasileira de Normas Tecnicas, Rio de Janeiro, Brazil.
  3. Bekdas, G. (2015), "Sizing optimization of truss structures using flower pollination algorithm", Appl. Soft Comput., 37, 322-331. https://doi.org/10.1016/j.asoc.2015.08.037.
  4. Bigham, A. and Gholizadeh, S. (2020), "Topology optimization of nonlinear single-layer domes by an improved electro-search algorithm and its performance analysis using statistical tests", Struct. Multidiscipl. Optimiz., 62(4), 1821-1848. https://doi.org/10.1007/s00158-020-02578-4.
  5. Camp, C. (2007) "Design of space trusses using Big Bang-Big Crunch optimization", J. Struct. Eng., 133(7), 999-1008. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:7(999).
  6. Cheng, M. (2016), "A hybrid harmony search algorithm for discrete sizing optimization of truss structure", Automation Construct., 69, 21-33. https://doi.org/10.1016/j.autcon.2016.05.023.
  7. Das, S. and Dhang, N. (2020), "Structural damage identification of truss structures using self-controlled multi-stage particle swarm optimization", Smart Struct. Syst., 25(3), 345-368. https://doi.org/10.12989/sss.2020.25.3.345.
  8. Dede, T., Kankal M., Vosoughi A.R., Grzywinski M. and Kripka M. (2016), "Artificial intelligence applications in civil engineering", Hindawi, 2019, https://doi.org/10.1155/2019/8384523.
  9. Fakury, R.H., Silva, A.L.R.C. and Caldas, R.B. (2016), Dimensionamento de Elementos Estruturais de Aco e Concreto. Pearson University.
  10. Gholizadeh, S., Davoudi, H. and Fattahi, F. (2017), "Design of steel frames by an enhanced moth-flame optimization algorithm", Steel Compos. Struct., 24(1), 129-140. https://doi.org/10.12989/scs.2017.24.1.129.
  11. Gholizadeh, S., Razavi, N. and Shojaei, E. (2019), "Improved black hole and multiverse algorithms for discrete sizing optimization of planar structures", Eng. Optimiz., 51(10), 1645-1667. https://doi.org/10.1080/0305215X.2018.1540697.
  12. Gholizadeh, S., Danesh, M. and Gheyratmand, C. (2020), "A new Newton metaheuristic algorithm for discrete performance-based design optimization of steel moment frames", Comput. Struct., 234, 106250. https://doi.org/10.1016/j.compstruc.2020.106250.
  13. Gomes, H. (2011), "Truss optimization with dynamic constraints using a particle swarm algorithm", Expert Syst. Appl., 38(1), 957-968. https://doi.org/10.1016/j.eswa.2010.07.086.
  14. Grzywinski, M., Dede, T. and O zdemir, Y.I. (2019), "Optimization of the braced dome structures by using Jaya algorithm with frequency constraints", Steel Compos. Struct., 30(1), 47-55. https://doi.org/10.12989/scs.2019.30.1.047.
  15. Ho-huu, V. (2015), "An improved constrained differential evolution using discrete variables (D-ICDE) for layout optimization of truss structures", Expert Syst. Appl., 42(20), 7057-7069. https://doi.org/10.1016/j.eswa.2015.04.072.
  16. Jain, M. (2019), "A novel nature-inspired algorithm for optimization: Squirrel search algorithm", Swarm Evolut. Comput., 44, 148-175. https://doi.org/10.1016/j.swevo.2018.02.013.
  17. Jia, G. (2013), "An improved (μ+ λ)-constrained differential evolution for constrained optimization", Information Sciences, 222, 302-322. https://doi.org/10.1016/j.ins.2012.01.017.
  18. Kaveh, A. and Talatahari, S. (2009) "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures", Comput. Struct., 87(5-6), 267-283. https://doi.org/10.1016/j.compstruc.2009.01.003.
  19. Kaveh, A. and Zolghadr, A. (2013) "Topology optimization of trusses considering static and dynamic constraints using the CSS", Appl. Soft Comput., 13(5), 2727-2734. https://doi.org/10.1016/j.asoc.2012.11.014.
  20. Khatibinia, M. and Yazdani, H. (2018) "Accelerated multigravitational search algorithm for size optimization of truss structures", Swarm Evolut. Comput., 38, 109-119. https://doi.org/10.1016/j.swevo.2017.07.001.
  21. Kirkpatrick, S. (1983), "Optimization by simulated annealing", Science, 220(4598), 671-680. https://10.1126/science.220.4598.671.
  22. Kripka, M. (2004), "Discrete optimization of trusses by simulated annealing", J. Brazil. Soc. Mech. Sci. Eng., 26(2), 170-173. https://doi.org/10.1590/S1678-58782004000200008.
  23. Lee, K. (2005), "The harmony search heuristic algorithm for discrete structural optimization", Eng. Optimiz, 37(7), 663-684. https://doi.org/10.1080/03052150500211895.
  24. Li, L. (2009), "A heuristic particle swarm optimization method for truss structures with discrete variables", Comput. Struct., 87(7-8), 435-443. https://doi.org/10.1016/j.compstruc.2009.01.004.
  25. Luh, G. and Lin, C. (2008) "Optimal design of truss structures using ant algorithm", Struct. Multidiscipl. Optimiz., 36(4), 365-379. https://doi.org/10.1007/s00158-007-0175-6.
  26. Mashayekhi, M. and Yousefi, R. (2021), "Topology and size optimization of truss structures using an improved crow search algorithm", Struct. Eng. Mech., 77(6), 779-795. https://doi.org/10.12989/sem.2021.77.6.779.
  27. Miguel, L.F.F. and Miguel, L.F.F. (2012), "Shape and size optimization of truss structures considering dynamic constraints through modern metaheuristic algorithms", Expert Syst. Appl., 39(10), 9458-9467. https://doi.org/10.1016/j.eswa.2012.02.113.
  28. Nguyen, V. and Lee, D. (2021), "Large-scaled truss topology optimization with filter and iterative parameter control algorithm of Tikhonov regularization", Steel Compos. Struct., 39(5), 511-528. https://doi.org/10.12989/scs.2021.39.5.511.
  29. Rajeev, S. and Krishnamoorthy, C. (1992), "Discrete optimization of structures using genetic algorithms", J. Struct. Eng., 118(5), 1233-1250. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:5(1233).
  30. Ringertz, U. (1988), "On methods for discrete structural optimization", Eng. Optimiz., 13(1), 47-64. https://doi.org/10.1080/03052158808940946.
  31. Sonmez, M. (2011), "Artificial Bee Colony algorithm for optimization of truss structures", Appl. Soft Comput., 11(2), 2406-2418. https://doi.org/10.1016/j.asoc.2010.09.003.
  32. Tejani, G. (2018), "Truss optimization with natural frequency bounds using improved symbiotic organisms search", Knowledge-Based Syst, 143, 162-178. https://doi.org/10.1016/j.knosys.2017.12.012.
  33. Topping, B. (1983), "Shape optimization of skeletal structures: a review", J. Struct. Eng., 109(8), 1933-1951. https://doi.org/10.1061/(ASCE)0733-9445(1983)109:8(1933).
  34. Xiang, B. (2009), "Optimization of trusses using simulated annealing for discrete variables", 2009 International Conference on Image Analysis and Signal Processing. IEEE, 410-414. https://10.1109/IASP.2009.5054647.
  35. Yang, X. (2012), "Flower pollination algorithm for global optimization", International Conference on Unconventional Computing and Natural Computation. Springer, 240-249. https://doi.org/10.1007/978-3-642-32894-7.
  36. Yeh, I. (1999), "Hybrid genetic algorithms for optimization of truss structures", Comput. Aid. Civil Infrastruct. Eng., 14(3), 199-206. https://doi.org/10.1111/0885-9507.00141.