Structural Engineering and Mechanics

  • 제61권3호
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  • Pages.359-370
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  • 2017
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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

An integrated particle swarm optimizer for optimization of truss structures with discrete variables

  • Mortazavi, Ali (Department of Civil Engineering, Ege University) ;
  • Togan, Vedat (Department of Civil Engineering, Karadeniz Technical University) ;
  • Nuhoglu, Ayhan (Department of Civil Engineering, Ege University)
  • 투고 : 2016.07.01
  • 심사 : 2016.11.06
  • 발행 : 2017.02.10
⟨ 이전 논문 다음 논문 ⟩

초록

This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.

키워드

참고문헌

  1. Alaimo, A., Milazzo, A. and Orlando, C. (2016), "Nonlinear model based particle swarm optimization of PID shimmy damping control", Adv. Aircr. Spacecrt. Sci., 3(2), 211-214. https://doi.org/10.12989/aas.2016.3.2.211
  2. Camp, C.V. and Bichon, B.J. (2004), "Design of space trusses using ant colony optimization", J. Struct. Eng., 130, 741-751. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:5(741)
  3. Chen, D. and Zhao, C. (2009), "Particle swarm optimization with adaptive population size and its application", App. Soft Comput., 9, 39-48. https://doi.org/10.1016/j.asoc.2008.03.001
  4. Coello Coello, C.A. (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
  5. Deb, K. and Gulati, S. (2001), "Design of truss-structures for minimum weight using genetic algorithms", Finite Elem. Anal. Des., 37, 447-465. https://doi.org/10.1016/S0168-874X(00)00057-3
  6. Dizangian, B. and Ghasemi, M.R. (2016), "An efficient method for reliable optimum design of trusses", Steel Compos. Struct., 21(5), 1069-1084. https://doi.org/10.12989/scs.2016.21.5.1069
  7. Erbatur, F., Hasancebi, O., Tutuncu, I. and Kilic, H. (2000), "Optimal design of planar and space structures with genetic algorithms", Comput. Struct., 75, 209-224. https://doi.org/10.1016/S0045-7949(99)00084-X
  8. Fan, Q. and Yan, X. (2014), "Self-adaptive particle swarm optimization with multiple velocity strategies and its application for p-Xylene oxidation reaction process optimization", Chemom. Intell. Lab. Syst., 139, 15-25. https://doi.org/10.1016/j.chemolab.2014.09.002
  9. Gholizadeh, S., Salajegheh, E. and Torkzadeh, P. (2008), "Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network", J. Sound Vib., 312, 316-331. https://doi.org/10.1016/j.jsv.2007.10.050
  10. Hajela, P. and Lee, E. (1995), "Genetic algorithms in truss topological optimization", Int. J. Solid. Struct., 32, 3341-3357. https://doi.org/10.1016/0020-7683(94)00306-H
  11. 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
  12. Hasancebi, O. and Erbatur, F. (2002), "On efficient use of simulated annealing in complex structural optimization problems", Acta Mech., 157, 27-50. https://doi.org/10.1007/BF01182153
  13. Hasancebi, O., Teke, T. and Pekcan, O. (2013), "A bat-inspired algorithm for structural optimization", Comput. Struct., 128, 77-90. https://doi.org/10.1016/j.compstruc.2013.07.006
  14. He, R.S. and Hwang, S.F. (2007), "Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis", Eng. Appl. Artif. Intell., 20, 980-992. https://doi.org/10.1016/j.engappai.2006.11.020
  15. He, S., Prempain, E. and Wu, Q.H. (2004), "An improved particle swarm optimizer for mechanical design optimization problems", Eng. Optim., 36, 585-605. https://doi.org/10.1080/03052150410001704854
  16. Kaveh, A. and Talatahari, S. (2009a), "A particle swarm ant colony optimization for truss structures with discrete variables", J. Constr. Steel Res., 65, 1558-1568. https://doi.org/10.1016/j.jcsr.2009.04.021
  17. Kaveh, A. and Talatahari, S. (2009b), "Size optimization of space trusses using Big Bang-Big crunch algorithm", Comput. Struct., 87, 1129-1140. https://doi.org/10.1016/j.compstruc.2009.04.011
  18. Kaveh, A., Kalatjari, V. and Talebpour, M. (2016), "Optimal design of steel towers using a multi-metaheuristic based search method", Period. Polytech. Civil Eng., doi:10.3311/PPci.8222.
  19. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of IEEE International Conference on Neural Networks, Perth, WA.
  20. Lee, K.S. and Geem, Z.W. (2004), "A new structural optimization method based on the harmony search algorithm", Comput. Struct., 82, 781-798. https://doi.org/10.1016/j.compstruc.2004.01.002
  21. Lee, K.S., Geem, Z.W., Lee, S.H. and Bae, K.W. (2005), "The harmony search heuristic algorithm for discrete structural optimization", Eng. Optim., 37, 663-684. https://doi.org/10.1080/03052150500211895
  22. Li, J.P. (2015), "Truss topology optimization using an improved species-conserving genetic algorithm", Eng. Optim., 47, 107-128. https://doi.org/10.1080/0305215X.2013.875165
  23. Li, L.J., Huang, Z.B. and Liu, F. (2009), "A heuristic particle swarm optimization method for truss structures with discrete variables", Comput. Struct., 87, 435-443. https://doi.org/10.1016/j.compstruc.2009.01.004
  24. Li, N.J., Wang, W.J., James Hsu, C.C., Chang, W., Chou, H.G. and Chang, J.W. (2014), "Enhanced particle swarm optimizer incorporating a weighted particle", Neurocomput., 124, 218-227. https://doi.org/10.1016/j.neucom.2013.07.005
  25. Nickabadi, A., Ebadzadeh, M.M. and Safabakhsh, R. (2011), "A novel particle swarm optimization algorithm with adaptive inertia weight", Appl. Soft Comput., 11, 3658-3670. https://doi.org/10.1016/j.asoc.2011.01.037
  26. Perez, R.E. and Behdinan, K. (2007), "Particle swarm approach for structural design optimization", Comput. Struct., 85, 1579-1588. https://doi.org/10.1016/j.compstruc.2006.10.013
  27. Rahami, H., Kaveh, A. and Gholipour, Y. (2008), "Sizing, geometry and topology optimization of trusses via force method and genetic algorithm", Eng. Struct., 30, 2360-2369. https://doi.org/10.1016/j.engstruct.2008.01.012
  28. Rajeev, S. and Krishnamoorthy, C. (1992), "Discrete optimization of structures using genetic algorithms", J. Struct. Eng., 118, 1233-1250. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:5(1233)
  29. Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M. (2012), "Mine blast algorithm for optimization of truss structures with discrete variables", Comput. Struct., 102-103, 49-63.
  30. Sadollah, A., Eskandar, H., Bahreininejad, A. and Kim, J.H. (2015), "Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures", Comput. Struct., 149, 1-16. https://doi.org/10.1016/j.compstruc.2014.12.003
  31. Sonmez, M. (2011), "Artificial bee colony algorithm for optimization of truss structures", Appl. Soft Comput., 11, 2406-2418. https://doi.org/10.1016/j.asoc.2010.09.003
  32. Togan, V. and Daloglu, A.T. (2006), "Optimization of 3d trusses with adaptive approach in genetic algorithms", Eng. Struct., 28, 1019-1027. https://doi.org/10.1016/j.engstruct.2005.11.007
  33. Togan, V. and Daloglu, A.T. (2008), "An improved genetic algorithm with initial population strategy and self-adaptive member grouping", Comput. Struct., 86, 1204-1218. https://doi.org/10.1016/j.compstruc.2007.11.006
  34. Wu, S.J. and Chow, P.T. (1995), "Steady-state genetic algorithms for discrete optimization of trusses", Comput. Struct., 56, 979-991. https://doi.org/10.1016/0045-7949(94)00551-D
  35. Zheng, Y.J. (2015), "Water wave optimization: a new natureinspired metaheuristic", Comput. Oper. Res., 55, 1-11. https://doi.org/10.1016/j.cor.2014.10.008

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