A ground-structure-based representation with an element-removal algorithm for truss topology optimization

  • Alin Shakya
  • Pruettha Nanakorn
  • Wasuwat Petprakob
RESEARCH PAPER
  • 97 Downloads

Abstract

In this study, a new ground-structure-based representation for truss topology optimization is proposed. The proposed representation employs an algorithm that removes unwanted elements from trusses to obtain the final trusses. These unwanted elements include kinematically unstable elements and useless zero-force elements. Since the element-removal algorithm is used in the translation of representation codes into corresponding trusses, this results in more representation codes in the search space that are mapped into kinematically stable and efficient trusses. Since more representation codes in the search space represent stable and efficient trusses, the strategy increases meaningful competition among representation codes. This remapping strategy alleviates the problem of having large search spaces using ground structures, and encourages faster convergences. To test the effectiveness of the proposed representation, it is used with a simple multi-population particle swarm optimization algorithm to solve several truss topology optimization problems. It is found that the proposed representation can significantly improve the performance of the optimization process.

Keywords

Truss topology optimization Representation Remapping Kinematic stability Zero-force element Element removal Particle swarm optimization Multiple populations 

Notes

Acknowledgements

The authors are grateful to the Thailand Research Fund for the financial support for this study (Contract Number: RMU5380026). A scholarship under the Graduate Scholarship Program for Excellent Foreign Students by Sirindhorn International Institute of Technology (SIIT) for the first author is greatly appreciated.

References

  1. Beckers M, Fleury C (1997) A primal-dual approach in truss topology optimization. Comput Struct 64(1–4):77–88CrossRefMATHGoogle Scholar
  2. Bołbotowski K, Sokół T (2016) New method of generating Strut and Tie models using truss topology optimization. In: Advances in Mechanics: Theoretical, Computational and Interdisciplinary Issues - 3rd Polish Congress of Mechanics, PCM 2015 and 21st International Conference on Computer Methods in Mechanics, CMM 2015, CRC Press/Balkema, Gdansk, 8–11 September 2015Google Scholar
  3. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73.  https://doi.org/10.1109/4235.985692 CrossRefGoogle Scholar
  4. Deb K, Gulati S (2001) Design of truss-structures for minimum weight using genetic algorithms. Finite Elem Anal Des 37(5):447–465.  https://doi.org/10.1016/S0168-874X(00)00057-3 CrossRefMATHGoogle Scholar
  5. Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000, IEEE Computer Society, San Diego, 16–19 July 2000Google Scholar
  6. Faramarzi A, Afshar MH (2012) Application of cellular automata to size and topology optimization of truss structures. Scientia Iranica 19(3):373–380.  https://doi.org/10.1016/j.scient.2012.04.009 CrossRefGoogle Scholar
  7. Gilbert M, Tyas A (2003) Layout optimization of large-scale pin-jointed frames. Engineering Computations (Swansea, Wales) 20(7–8):1044–1064.  https://doi.org/10.1108/02644400310503017 CrossRefMATHGoogle Scholar
  8. Hagishita T, Ohsaki M (2009) Topology optimization of trusses by growing ground structure method. Struct Multidiscip Optim 37(4):377–393.  https://doi.org/10.1007/s00158-008-0237-4 CrossRefGoogle Scholar
  9. Hajela P, Lee E (1995) Genetic algorithms in truss topological optimization. Int J Solids Struct 32(22):3341–3357MathSciNetCrossRefMATHGoogle Scholar
  10. Hasançebi O, Erbatur F (2002) Layout optimisation of trusses using simulated annealing. Adv Eng Softw 33(7–10):681–696.  https://doi.org/10.1016/S0965-9978(02)00049-2 CrossRefMATHGoogle Scholar
  11. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99.  https://doi.org/10.1016/j.engappai.2006.03.003 MathSciNetCrossRefGoogle Scholar
  12. Jiang Y, Hu T, Huang C, Wu X (2007) An improved particle swarm optimization algorithm. Appl Math Comput 193(1):231–239.  https://doi.org/10.1016/j.amc.2007.03.047 MATHGoogle Scholar
  13. Kaveh A, Kalatjari V (2003) Topology optimization of trusses using genetic algorithm, force method and graph theory. Int J Numer Methods Eng 58(5):771–791.  https://doi.org/10.1002/nme.800 CrossRefMATHGoogle Scholar
  14. Liu X, Liu H, Duan H (2007) Particle swarm optimization based on dynamic niche technology with applications to conceptual design. Adv Eng Softw 38(10):668–676.  https://doi.org/10.1016/j.advengsoft.2006.10.009 CrossRefGoogle Scholar
  15. Luh GC, Lin CY (2011) Optimal design of truss-structures using particle swarm optimization. Comput Struct 89(23–24):2221–2232.  https://doi.org/10.1016/j.compstruc.2011.08.013 CrossRefGoogle Scholar
  16. Martínez P, Martí P, Querin OM (2007) Growth method for size, topology, and geometry optimization of truss structures. Struct Multidiscip Optim 33(1):13–26.  https://doi.org/10.1007/s00158-006-0043-9 CrossRefGoogle Scholar
  17. Nimtawat A, Nanakorn P (2009) Automated layout design of beam-slab floors using a genetic algorithm. Comput Struct 87(21–22):1308–1330.  https://doi.org/10.1016/j.compstruc.2009.06.007 CrossRefGoogle Scholar
  18. Nimtawat A, Nanakorn P (2010) A genetic algorithm for beam-slab layout design of rectilinear floors. Eng Struct 32(11):3488–3500.  https://doi.org/10.1016/j.engstruct.2010.07.018 CrossRefGoogle Scholar
  19. Niu B, Zhu Y, He X, Wu H (2007) MCPSO: A multi-swarm cooperative particle swarm optimizer. Appl Math Comput 185(2):1050–1062.  https://doi.org/10.1016/j.amc.2006.07.026 MATHGoogle Scholar
  20. Niu B, Zhu Y, He X, Shen H (2008) A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing. Neurocomputing 71(7–9):1436–1448.  https://doi.org/10.1016/j.neucom.2007.05.010 CrossRefGoogle Scholar
  21. Ohsaki M, Fujisawa K, Katoh N, Kanno Y (1999) Semi-definite programming for topology optimization of trusses under multiple eigenvalue constraints. Comput Methods Appl Mech Eng 180(1–2):203–217.  https://doi.org/10.1016/S0045-7825(99)00056-0 CrossRefMATHGoogle Scholar
  22. Rajan SD (1995) Sizing, shape, and topology design optimization of trusses using genetic algorithm. J Struct Eng 121(10):1480–1487.  https://doi.org/10.1061/(ASCE)0733-9445(1995)121:10(1480) CrossRefGoogle Scholar
  23. Richardson JN, Adriaenssens S, Bouillard P, Coelho RF (2012) Multiobjective topology optimization of truss structures with kinematic stability repair. Struct Multidiscip Optim 46(4):513–532.  https://doi.org/10.1007/s00158-012-0777-5 CrossRefMATHGoogle Scholar
  24. Sokół T (2011) A 99 line code for discretized michell truss optimization written in mathematica. Struct Multidiscip Optim 43(2):181–190.  https://doi.org/10.1007/s00158-010-0557-z CrossRefMATHGoogle Scholar
  25. Wu CY, Tseng KY (2010) Truss structure optimization using adaptive multi-population differential evolution. Struct Multidiscip Optim 42(4):575–590.  https://doi.org/10.1007/s00158-010-0507-9 CrossRefGoogle Scholar
  26. Zhao SZ, Suganthan PN, Pan QK, Fatih Tasgetiren M (2011) Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38(4):3735–3742.  https://doi.org/10.1016/j.eswa.2010.09.032 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Civil Engineering and Technology, Sirindhorn International Institute of TechnologyThammasat UniversityPathumthaniThailand

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