Structural Engineering and Mechanics

  • 제77권3호
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  • Pages.417-439
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  • 2021
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A comparative study of multi-objective evolutionary metaheuristics for lattice girder design optimization

  • 투고 : 2020.06.01
  • 심사 : 2020.11.13
  • 발행 : 2021.02.10
⟨ 이전 논문 다음 논문 ⟩

초록

The geometric nonlinearity has been successfully integrated with the design of steel structural system. Thus, the tubular lattice girder, one application of steel structural systems have already been optimized to obtain an economic design following the completion of computationally expensive design procedure. In order to decrease its computing cost, this study proposes to employ five multi-objective metaheuristics for the design optimization of geometrically nonlinear tubular lattice girder. Then, the employed multi-objective optimization algorithms (MOAs), NSGAII, PESAII, SPEAII, AbYSS and MoCell are evaluated considering their computing performances. For an unbiased evaluation of their computing performance, a tubular lattice girder with varying size-shape-topology and a benchmark truss design with 17 members are not only optimized considering the geometrically nonlinear behavior, but three benchmark mathematical functions along with the four benchmark linear design problems are also included for the comparison purpose. The proposed experimental study is carried out by use of an intelligent optimization tool named JMetal v5.10. According to the quantitative results of employed quality indicators with respect to a statistical analysis test, MoCell is resulted with an achievement of showing better computing performance compared to other four MOAs. Consequently, MoCell is suggested as an optimization tool for the design of geometrically nonlinear tubular lattice girder than the other employed MOAs.

키워드

참고문헌

  1. Arora, J. (2011), Introduction to Optimum Design, Third edition, Academic Press, MI, USA.
  2. Atmaca, B., Dede, T. and Grzywinski, M. (2020), "Optimization of cables size and prestressing force for a single pylon cable-stayed bridge with Jaya algorithm", Steel Comp. Struct., 34(6), 853-862. https://doi.org/10.12989/scs.2020.34.6.853.
  3. Belegundu, A.D. and Chandrupatla T.R. (2011), Optimization Concepts and Applications in Engineering, Second Edition, Cambridge University Press, United Kingdom.
  4. Chiandussi, G., Codegone, M., Ferrero, S. and Varesio, F.E., (2012), "Comparison of multi-objective optimization methodologies for engineering applications", Comp. Math. App., 63(5), 912-942. https://doi.org/10.1016/j.camwa.2011.11.057.
  5. Coello, C.A.C (2001), "A short tutorial on evolutionary multiobjective optimization", Proceedings, First International Conference on Evolutionary Multi-Criterion Optimization, Zurich, March.
  6. Coello, C.A.C. and Pulido, G. (2005), "Multiobjective Structural Optimization Using a Microgenetic Algorithm", Struct. Mult. Optim., 30, 388-403. https://doi.org/10.1007/s00158-005-0527-z.
  7. Coello, C.A.C., Lamont, G.B. and Van Veldhuisen D.A. (2007), Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, London, United Kingdom.
  8. Deb, K., Thiele, L., Laumanns, M. and Zitzler, E. (2001), "Scalable Test Problems for Evolutionary Multi-Objective Optimization", Evolutionary Multiobjective Optimization, 105-145. Zurich, Switzerland
  9. Deb, K., Pratap, A., Agarwal, S. and Meyarivan T. (2002), "A fast and elitist multi-objective genetic algorithm: NSGA-II", IEEE Trans. Evol. Comp., 6(2), 182-197. https://doi.org/10.1109/4235.996017.
  10. Dede, T., Grzywinski, M. and Selejdak, J. (2020), "Continuous size optimization of large-scale dome structures with dynamic constraints", Struct. Eng. Mech., 73(4), 397-405. https://doi.org/10.12989/sem.2020.73.4.397.
  11. Durillo, J.J. and Nebro A.J. (2011), "jMetal: A java framework for multi-objective optimization", Adv. Eng. Software, 42(10), 760-771. https://doi.org/10.1016/j.advengsoft.2011.05.014.
  12. Durillo, J.J., Nebro, A.J. and Alba, E. (2010), "The jMetal framework for multi-objective optimization: Design and architecture", CEC 2010, 4138-4325. https://doi.org/10.1109/CEC.2010.5586354.
  13. Farhang-Mehr, A. and Azarm, S. (2002) "Entropy-based multi-objective genetic algorithm for design optimization", Struct. Mult. Optim., 24(5), 351-361. https://doi.org/10.1007/s00158-002-0247-6.
  14. Gholizadeh, S., Danesh, M. and Gheyratmand, C. (2020), "A new Newton metaheuristic algorithm for discrete performance-based design optimization of steel moment frames", Comp. Struct., 234, https://doi.org/10.1016/j.compstruc.2020.106250.
  15. Got, A., Moussaoui A. and Zouache, D. (2020), "A guided population archive whale optimization algorithm for solving multiobjective optimization problems", Expert Syst. App., 141. https://doi.org/10.1016/j.eswa.2019.112972.
  16. Hernandez, S., Brebbia, C.A. and Wilde, W.P. (2012), Computer Aided Optimum Design in Engineering XII, WIT press, United Kingdom.
  17. Ho-Huu, V., Hartjes, S., Visser, H.G. and Curran, R. (2018), "An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization", Exp. Sys. with App., 92, 430-446. https://doi.org/10.1016/j.eswa.2017.09.051.
  18. Jahangiri, M., Hadianfard, M.A., Najafgholipour, M.A., Jahangiri, M. and Gerami, M.R. (2020), "Interactive autodidactic school: A new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems", Comp. Struct., 235. https://doi.org/10.1016/j.compstruc.2020.106268.
  19. Kaveh, A. and Laknejadi, K. (2011a), "A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization", Expert Syst. Appl., 12(38), 15475-15488. https://doi.org/10.1016/j.eswa.2011.06.012.
  20. Kaveh, A. and Laknejadi, K. (2011b), "A hybrid multi-objective particle swarm optimization and decision making procedure for optimal design of truss structures", Iranian J. Sci. Technol., 35(C2), 137-154.
  21. Kaveh, A. and Laknejadi, K. and Alinejad B. (2012), "Performance based multi-objective optimization of large steel structures", Acta Mechanica, 2(223), 355-369. https://doi.org/10.1007/s00707-011-0564-1.
  22. Kaveh, A. and Laknejadi, K. (2013a), "A hybrid evolutionary graph based multi-objective algorithm for layout optimization of truss structures", Acta Mechanica, 224, 343-364. https://doi.org/10.1007/s00707-012-0754-5.
  23. Kaveh, A. and Laknejadi, K. (2013b), "A new multi-swarm multiobjective optimization method for structural design", Adv. Eng. Software, 58, 54-69. https://doi.org/10.1016/j.advengsoft.2013.01.004.
  24. Kaveh, A., Kalateh-Ahani, M. and Fahimi-Farzam, M. (2013), "Constructability optimal design of reinforced concrete retaining walls using a multi-objective genetic algorithm", Struct. Eng. Mech., 2(47), 227-245. https://doi.org/10.12989/sem.2013.47.2.227.
  25. Kaveh, A. and Massoudi, M.S. (2014), "Multi-objective optimization using Charged System Search", Scientia Iranica, 6(21), 1845-1860. http://scientiairanica.sharif.edu/issue_153_159.html.
  26. Kaveh, A., Fahimi-Farzam, M. and Kalateh-Ahani, M. (2015), "Performance-based multi-objective optimal design of steel frame structures: nonlinear dynamic procedure", Scientia Iranica, 2(22), 373-387. http://scientiairanica.sharif.edu/issue_160_162.html.
  27. Kaveh, A. and Bakhshpoori, T. (2016), "An efficient and simplified multi-objective cuckoo search algorithm for design optimization", Adv. Comput. Design, 1(1), 87-103. https://doi.org/10.12989/acd.2016.1.1.087.
  28. Kaveh, A., Mahdipour Moghanni, R. and Javadi, S.M. (2019), "Ground motion record selection using multi-objective optimization algorithms: a comparative study", Periodica Polytechnica Civil Eng., 63(3), 812-822. https://doi.org/10.3311/PPci.14354.
  29. Kaveh, A. and Mahdavi, V.R., (2019), "Multi-objective colliding bodies optimization algorithm for design of trusses", J. Comp. Des. Eng., 6(1), 49-59. https://doi.org/10.1016/j.jcde.2018.04.001.
  30. Kaveh, A., and Ilchi Ghazaan M. (2020), "A new VPS-based algorithm for multi-objective optimization problems", Eng. Comput., 36 ,1029-1040. https://doi.org/10.1007/s00366-019-00747-8.
  31. Khot, N.S. and Berke, L. (1984), "Structural optimization using optimality criteria methods", Proceeding of New directions in optimum structural design, John Wiley and Sons Inc., NJ, USA.
  32. Kooshkbaghi, M. and Kaveh, A. (2020), "Sizing optimization of truss structures with continuous variables by artificial coronary circulation system algorithm", Iran J. Sci. Tech. Trans. Civ. Eng., 44, 1-20. https://doi.org/10.1007/s40996-019-00254-2.
  33. Kooshkbaghi, M., Kaveh, A. and Zarfam, P. (2020), "Different discrete ACCS algorithms for optimal design of truss structures: A comparative study", Iran J. Sci. Tech. Trans. Civ. Eng., 44,49-68. https://doi.org/10.1007/s40996-019-00291-x.
  34. Lee, K.S. and Geem, Z.W., (2004), "A new structural optimization method based on harmony search algorithm", Comp. Struc., 82,781-798. https://doi.org/10.1016/j.compstruc.2004.01.002.
  35. Li, L.J., Huang, Z.B., Liu, F. and Wu, Q.H. (2007), "A heuristic Particle Swarm Optimizer for Optimization of Pin Connected Structures", Comp. Struct., 85, 340-349. https://doi.org/10.1016/j.compstruc.2006.11.020.
  36. Makoto, O. (2011), Optimization of Finite Dimensional Structures, CRC Press, Florida, USA. https://doi.org/10.1201/EBK1439820032.
  37. Mortazavi, A. (2020), "Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm", Adv. Eng. Soft., 142. https://doi.org/10.1016/j.advengsoft.2020.102790.
  38. Omidinasab, F. and Goodarzimehr, V. (2020). "A hybrid particle swarm optimization and genetic algorithm for truss structures with discrete variables", J. Appl. Comp. Mech., 6(3), 593-604. https://doi.org/10.22055/jacm.2019.28992.1531.
  39. Parejo, J.A., Ruiz-Cortes, A., Lozano, S. and Fernandez, P. (2012), "Metaheuristic optimization frameworks: A survey and benchmarking", Soft Comp., 16, 527-561. https://doi.org/10.1007/s00500-011-0754-8.
  40. Peter, W.C. and Klarbring A. (2009), An Introduction to Structural Optimization, 2009, Springer, London, United Kingdom. https://doi.org/10.1007/978-1-4020-8666-3.
  41. Rao, S.S. (1987), "Game theory approach for multiobjective structural optimization", Comp. Struct., 25, 119-127. https://doi.org/10.1016/0045-7949(87)90223-9.
  42. Reddy, M.J. and Kumar, D.N. (2007) "An efficient Multi-objective Optimization Algorithm Based on Swarm Intelligence for Engineering Design", Eng. Optimization, 39, 49-68. https://doi.org/10.1080/03052150600930493.
  43. Shahrouzi, M., Aghabaglou, M. and Rafiee, F. (2017), "ObserverTeacher-Learner-Based Optimization: An enhanced metaheuristic for structural sizing design", Struct. Eng. Mech., 62(5). https://doi.org/10.12989/sem.2017.62.5.537.
  44. Singiresu, S.R. (2009), Engineering Optimization: Theory and Practice, John Wiley, NJ, USA.
  45. Srinivas, N. and Deb, K. (1995), "Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation", 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221.
  46. Talaslioglu, T. (2011), "Multiobjective design optimization of grillage systems according to LRFD-AISC", Adv. Civil Eng., https://doi.org/10.1155/2011/932871.
  47. Talaslioglu, T. (2015), "Optimization of geometrically nonlinear lattice girders part i: considering member strengths", J. Civil Eng. Man., 21(4), 423-443. https://doi.org/10.3846/13923730.2014.890648.
  48. Talaslioglu, T. (2019a), "Optimal dome design considering member-related design constraints", Front. Struct. Civ. Eng., 13, 1150-1170. https://doi.org/10.1007/s11709-019-0543-5.
  49. Talaslioglu, T. (2019b), "Design Optimization of Tubular Lattice Girders", Adv. Steel Constr, 15(3), 274-287. https://doi.org/10.18057/IJASC.2019.15.3.8.
  50. Talaslioglu, T. (2019c) "Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology", Front. Struct. Civ. Eng., 13, 863-889. https://doi.org/10.1007/s11709-019-0523-9.
  51. Talaslioglu, T. (2019d). "A Unified Optimal Design Approach for Geometrically Nonlinear Skeletal Dome Structures" Periodica Poly. Civil Eng., 63(2), 518-540. https://doi.org/10.3311/PPci.13329.
  52. Tejani, G.G., Savsani, V.J., Patel, V.K. and Bureerat S. (2017) "Topology, shape, and size optimization of truss structures using modified teaching-learning based optimization", Adv. Comput. Design, 2(4), 313-331. https://doi.org/10.12989/acd.2017.2.4.313.
  53. Tejani, G.G., Pholdee, N., Bureerat, S. and Prayogo, D. (2018), "Multiobjective adaptive symbiotic organisms search for truss optimization problems", Know.-Based Syst., 161, 398-414. https://doi.org/10.1016/j.knosys.2018.08.005.
  54. Veldhuizen, D.A. and Lamont G.B. (1998), "Multi-objective evolutionary algorithm research: a history and analysis", Technical Report TR-98-03, Department of Electrical and Computer Engineering Graduate School of Engineering Air Force Institute of Technology, Wright-Patterson, Air Force Base, OH, USA.
  55. Zawidzki, M., Jankowski, L. (2019), "Multiobjective optimization of modular structures: Weight versus geometric versatility in a Truss‐Z system", Comput Aided Civ Inf. Struc., 34, 1026-1040. https://doi.org/10.1111/mice.12478.
  56. Zitzler, E. and Thiele L. (1999) "Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach", IEEE Trans. Evol. Comp., 3(4), 257-271. https://doi.org/10.1109/4235.797969.
  57. Zitzler, E., Deb, K. and Thieler, L. (2000) "Comparison of multiobjective evolutionary algorithms: Empirical results", IEEE Trans. Evol. Comp., 8, 173-195. https://doi.org/10.1162/106365600568202.
  58. JMetal v5.10 (2020), jMetal; jMetal, http://jmetal.sourceforge.net
  59. JMetal (2020), jMetal; Github, CA, USA. https://github.com/jMetal/.
  60. CRAN Package (2020), The R Project for Statistical Computing; The R Foundation. http://www.r-project.org.
  61. Basic MiKTeX 2.9.4521 package (2020), MikTeX; Christian Schenk. http://miktex.org.
  62. Java (2020), Java; Oracle, CA, USA. http://www.java.com.

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