Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Optimum design of steel frame structures by a modified dolphin echolocation algorithm

  • Received : 2015.03.04
  • Accepted : 2015.05.11
  • Published : 2015.08.10
⟨ Previous Next ⟩

Abstract

Dolphin echolocation (DE) optimization algorithm is a recently developed meta-heuristic in which echolocation behavior of Dolphins is utilized for seeking a design space. The computational performance of meta-heuristic algorithms is highly dependent to its internal parameters. But the computational time of adjusting these parameters is usually extensive. The DE is an efficient optimization algorithm as it includes few internal parameters compared with other meta-heuristics. In the present paper a modified Dolphin echolocation (MDE) algorithm is proposed for optimization of steel frame structures. In the MDE the step locations are determined using one-dimensional chaotic maps and this improves the convergence behavior of the algorithm. The effectiveness of the proposed MDE algorithm is illustrated in three benchmark steel frame optimization test examples. Results demonstrate the efficiency of the proposed MDE algorithm in finding better solutions compared to standard DE and other existing algorithms.

Keywords

References

  1. Alatas, B. (2010a), "Chaotic harmony search algorithms", Appl. Math Comput., 216, 2687-2699. https://doi.org/10.1016/j.amc.2010.03.114
  2. Alatas, B. (2010b), "Chaotic bee colony algorithms for global numerical optimization", Expert Syst. Appl., 37, 5682-5687. https://doi.org/10.1016/j.eswa.2010.02.042
  3. American Institutes of Steel Construction (AISC) (1989), Manual of Steel Construction-Allowable Stress Design, Ninth Edition, AISC, Inc., Chicago, Illinois, USA.
  4. American Institute of Steel Construction (AISC) (1991), Manual of Steel Construction-Load Resistance Factor Design, 3rd Edition, AISC, Chicago, IL, USA.
  5. ASCE 7-05 (2005), Minimum Design Loads for Building and Other Structures.
  6. Au, W.W.L. (1993), The Sonar of Dolphins, Springer, New York.
  7. Camp, C.V., Bichon, J. and Stovall, S.P. (2005), "Design of steel frames using ant colony optimization", J. Struct. Eng., 131(3), 369-379. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:3(369)
  8. Coelho, L. and Mariani, V.C. (2008), "Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization", Expert Syst. Appl., 34, 1905-1913. https://doi.org/10.1016/j.eswa.2007.02.002
  9. Degertekin, S.O. (2008), "Optimum design of steel frames using harmony search algorithm", Struct. Multidisc. Optim., 36, 393-401. https://doi.org/10.1007/s00158-007-0177-4
  10. Dumonteil, P. (1992), "Simple equations for effective length factors", Eng. J. AISC, 29, 111-115.
  11. Eberhart, R.C. and Kennedy, J. (1995), "A new optimizer using particle swarm theory", Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan.
  12. Gandomi, A.H. and Alavi, A.H. (2012), "Krill herd: A new bio-inspired optimization algorithm", Commun. Nonlin. Sci. Numer. Simul., 17(12), 4831-4835. https://doi.org/10.1016/j.cnsns.2012.05.010
  13. Gandomi, A.H., Yang, X.S., Talatahari, S. and Alavi, A.H. (2013), Metaheuristic Applications in Structures and Infrastructures, 1st. Elsevier Science Publishers B.V., Amsterdam.
  14. Gandomi, A.H., Yun, G.J., Yang, X.S. and Talatahari, S. (2013a), "Chaos-enhanced accelerated particle swarm algorithm", Commun. Nonlin. Sci. Numer. Simul., 18(2), 327-340. https://doi.org/10.1016/j.cnsns.2012.07.017
  15. Gandomi, A.H., Yang, X.S., Talatahari, S. and Alavi, A.H. (2013b), "Firefly algorithm with chaos", Commun. Nonlin. Sci. Numer. Simul., 18(1), 89-98. https://doi.org/10.1016/j.cnsns.2012.06.009
  16. Gandomi, A.H. and Yang, X.S. (2014), "Chaotic bat algorithm", J. Comput. Sci., 5, 224-232. https://doi.org/10.1016/j.jocs.2013.10.002
  17. Gharooni-fard, G., Moein-darbari, F., Deldari, H. and Morvaridi, A. (2010), "Scheduling of scientific workflows using a chaos-genetic algorithm", Procedia Comput. Sci., 1, 1445-1454. https://doi.org/10.1016/j.procs.2010.04.160
  18. Gholizadeh, S. and Fattahi, F. (2014), "Design optimization of tall steel buildings by a modified particle swarm algorithm", Struct. Des. Tall Spec. Buil., 23, 285-301. https://doi.org/10.1002/tal.1042
  19. Gholizadeh, S., Asadi, H. and Baghchevan, A. (2014), "Optimal design of truss structures by improved multi-objective firefly and bat algorithms", Int. J. Optim. Civil Eng., 4, 415-31.
  20. Goldberg, D.E. (1989), Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Boston.
  21. Gong, W. and Wang, S. (2009), "Chaos ant colony optimization and application", 4th Inter-national Conference on Internet Computing for Science and Engineering, 301-303.
  22. Hasancebi, O., Carbas, S., Dogan, E., Erdal, F. and Saka, M.P. (2010), "Comparison of non-deterministic search techniques in the optimum design of real size steel frames", Comput. Struct., 88, 1033-1048. https://doi.org/10.1016/j.compstruc.2010.06.006
  23. Hasancebi, O., Bahcecioglu, T., Kurc, O. and Saka, M.P. (2011), "Optimum design of high-rise steel buildings using an evolution strategy integrated parallel algorithm", Comput. Struct., 89, 2037-2051. https://doi.org/10.1016/j.compstruc.2011.05.019
  24. He, D., He, C., Jiang, L., Zhu, H. and Hu, G. (2001), "Chaotic characteristic of a one-dimensional iterative map with infinite collapses", IEEE Circuits Syst., 48, 900-906. https://doi.org/10.1109/81.933333
  25. Hellesland, J. (1994), "Review and evaluation of effective length formulas", Research Report, No. 94-102, University of Oslo.
  26. Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press.
  27. Kaveh, A. and Talatahari, S. (2009), "Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization", Stud. Compu. Intell., 239, 159-198. https://doi.org/10.1007/978-3-642-03450-3_5
  28. Kaveh, A. and Talatahari, S. (2010a), "A novel heuristic optimization method: charged system search", Acta Mech., 213, 267-286. https://doi.org/10.1007/s00707-009-0270-4
  29. Kaveh, A. and Talatahari, S. (2010b), "Optimum design of skeletal structures using imperialist competitive algorithm", Comput. Struct., 88, 1220-1229. https://doi.org/10.1016/j.compstruc.2010.06.011
  30. Kaveh, A. and Farhoudi, N. (2011), "A unified approach to parameter selection in meta-heuristic algorithms for layout optimization", J. Constr. Steel Res., 67, 1453-1462. https://doi.org/10.1016/j.jcsr.2011.03.019
  31. Kaveh, A. and Talatahari, S. (2012), "Charged system search for optimal design of planar frame structures", Appl. Soft Comput., 12, 382-393. https://doi.org/10.1016/j.asoc.2011.08.034
  32. Kaveh, A. and Farhoudi, N. (2013), "A new optimization method: Dolphin echolocation", Adv. Eng. Softw., 59, 53-70. https://doi.org/10.1016/j.advengsoft.2013.03.004
  33. Li, Y., Deng, S. and Xiao, D. (2011), "A novel Hash algorithm construction based on chaotic neural network", Neural Comput. Appl., 20, 133-41. https://doi.org/10.1007/s00521-010-0432-2
  34. Lin, J.H., Chou, C.W., Yang, C.H. and Tsai, H.L. (2012), "A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems", J. Comput. Inf. Tech., 2, 56-63.
  35. MATLAB (2009), The language of technical computing (software), The MathWorks.
  36. Mingjun, J. and Huanwen, T. (2004), "Application of chaos in simulated annealing", Chaos Sol. Fract., 21, 933-941. https://doi.org/10.1016/j.chaos.2003.12.032
  37. Pecora, L. and Carroll, T. (1990), "Synchronization in chaotic system", Phys. Rev. Lett., 4, 821-824.
  38. Talatahari, S., Sheikholeslami, R., Farahmand Azar, B. and Gandomi, A.H. (2012), "Imperialist competitive algorithm combined with chaos for global optimization", Commun. Nonlin. Sci. Numer. Simul., 17(3), 1312-1319. https://doi.org/10.1016/j.cnsns.2011.08.021
  39. Talatahari, S., Hosseini, A., Mirghaderi, S.R. and Rezazadeh, F. (2014), "Optimum performance-based seismic design using a hybrid optimization algorithm", Math. Probl. Eng., 2014, 1-8.
  40. Talatahari, S., Gandomi, A.H., Yang, X.S. and Deb, S. (2015), "Optimum design of frame structures using the eagle strategy with differential evolution", Eng. Struct., 91, 16-25. https://doi.org/10.1016/j.engstruct.2015.02.026
  41. Togan, V. (2012), "Design of planar steel frames using Teaching-Learning based optimization", Eng. Struct., 34, 225-232. https://doi.org/10.1016/j.engstruct.2011.08.035
  42. Wang, G.G., Guo, L., Gandomi, A.H., Hao, G.S. and Wang, H. (2014), "Chaotic Krill Herd algorithm", Inform. Sci., 274, 17-34. https://doi.org/10.1016/j.ins.2014.02.123
  43. Wang, G.G., Deb, S., Gandomi, A.H., Zhaojun, Z. and Alavi A.H. (2014), "A novel cuckoo search with chaos theory and elitism scheme", 2014 International Conference on Soft Computing & Machine Intelligence, IEEE, New Delhi, 64-69.
  44. Yang, X.S. (2008), Nature-Inspired Metaheuristic Algorithms, Luniver Press.
  45. Yang, X.S. (2010), "A new meta-heuristic bat-Inspired algorithm", Eds. Gonzalez JR et al., Nature inspired cooperative strategies for optimization (NISCO 2010), Studies in computational intelligence, Springer, Berlin.
  46. Yang, X.S., Cui, Z., Xiao, R., Gandomi, A.H. and Karamanoglu, M. (2013), Swarm Intelligence and Bio-Inspired Computation Theory and Application, 1st Elsevier Science Publishers B.V., Amsterdam.

Cited by

  1. Optimal design of double layer barrel vaults considering nonlinear behavior vol.58, pp.6, 2016, https://doi.org/10.12989/sem.2016.58.6.1109
  2. Seismic layout optimization of steel braced frames by an improved dolphin echolocation algorithm vol.54, pp.4, 2016, https://doi.org/10.1007/s00158-016-1461-y
  3. Natural Forest Regeneration Algorithm: A New Meta-Heuristic vol.40, pp.4, 2016, https://doi.org/10.1007/s40996-016-0042-z
  4. An Enhanced Imperialist Competitive Algorithm for optimum design of skeletal structures 2018, https://doi.org/10.1016/j.swevo.2017.12.001
  5. Optimal design of truss structures using a new optimization algorithm based on global sensitivity analysis vol.60, pp.6, 2016, https://doi.org/10.12989/sem.2016.60.6.1093
  6. Weight minimization of truss structures with sizing and layout variables using integrated particle swarm optimizer vol.23, pp.8, 2017, https://doi.org/10.3846/13923730.2017.1348982
  7. An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures 2018, https://doi.org/10.1080/0305215X.2017.1417402
  8. Optimum Design of Laterally Supported Castellated Beams Using Natural Forest Regeneration Algorithm vol.40, pp.3, 2016, https://doi.org/10.1007/s40996-016-0024-1
  9. Improved black hole and multiverse algorithms for discrete sizing optimization of planar structures pp.1029-0273, 2018, https://doi.org/10.1080/0305215X.2018.1540697
  10. Improved Fruit Fly Optimization Algorithm Incorporating Tabu Search for Optimizing the Selection of Elements in Trusses pp.1976-3808, 2018, https://doi.org/10.1007/s12205-017-2000-0
  11. An efficient hybrid of elephant herding optimization and cultural algorithm for optimal design of trusses pp.1435-5663, 2018, https://doi.org/10.1007/s00366-018-0631-5
  12. Hybrid Nelder–Mead Algorithm and Dragonfly Algorithm for Function Optimization and the Training of a Multilayer Perceptron pp.2191-4281, 2018, https://doi.org/10.1007/s13369-018-3536-0
  13. A three-stage damage detection method for large-scale space structures using forward substructuring approach and enhanced bat optimization algorithm pp.1435-5663, 2018, https://doi.org/10.1007/s00366-018-0636-0
  14. Optimum design of steel space structures using social spider optimization algorithm with spider jump technique vol.62, pp.3, 2015, https://doi.org/10.12989/sem.2017.62.3.259
  15. Design of steel frames by an enhanced moth-flame optimization algorithm vol.24, pp.1, 2015, https://doi.org/10.12989/scs.2017.24.1.129
  16. Design of lightweight mansard portal frames vol.24, pp.3, 2015, https://doi.org/10.12989/scs.2017.24.3.277
  17. Performance based discrete topology optimization of steel braced frames by a new metaheuristic vol.123, pp.None, 2015, https://doi.org/10.1016/j.advengsoft.2018.06.002
  18. Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information vol.2019, pp.None, 2015, https://doi.org/10.1155/2019/7189653
  19. Seismic performance of outrigger-belt truss system considering soil-structure interaction vol.11, pp.1, 2015, https://doi.org/10.1007/s40091-019-0215-7
  20. Geometry and Sizing Optimization of Steel Pitched Roof Frames with Tapered Members Using Nine Metaheuristics vol.43, pp.1, 2015, https://doi.org/10.1007/s40996-018-0132-1
  21. Topology optimization of nonlinear single-layer domes by an improved electro-search algorithm and its performance analysis using statistical tests vol.62, pp.4, 2015, https://doi.org/10.1007/s00158-020-02578-4
  22. A new second-order approximation method for optimum design of structures vol.19, pp.1, 2015, https://doi.org/10.1080/14488353.2020.1798039
  23. Topology and size optimization of truss structures using an improved crow search algorithm vol.77, pp.6, 2015, https://doi.org/10.12989/sem.2021.77.6.779
  24. Review of Metaheuristics Inspired from the Animal Kingdom vol.9, pp.18, 2021, https://doi.org/10.3390/math9182335
  25. Elephant clan optimization: A nature-inspired metaheuristic algorithm for the optimal design of structures vol.113, pp.no.pa, 2021, https://doi.org/10.1016/j.asoc.2021.107892