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Application of Teaching-Learning-Based-Optimization algorithm for the discrete optimization of truss structures

  • Structural Engineering
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Abstract

The aim of this study is to present a new efficient optimization algorithm called Teaching-Learning-Based Optimization (TLBO). The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several benchmark problem related truss structures with discrete design variables are used to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

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References

  • Camp, C. V. and Bichon, B. J. (2004). “Design of space trusses using ant colony optimization.” Journal of Structural Engineering, Vol. 130, No. 7, pp. 741–751.

    Article  Google Scholar 

  • Capriles, P. V. S. Z., Fonseca, L. G., Barbosa, H. J. C., and Lemonge, A. C. C. (2007). “Rank-based ant colony algorithms for truss weight minimization with discrete variables.” Communications in Numerical Methods in Engineering, Vol. 23, pp. 553–575.

    Article  MATH  MathSciNet  Google Scholar 

  • Cerny, V. (1985). “Thermodynamical approach to the travelling saleman problem: An efficient simulation algorithm.” J. Optim. Theory Appl., Vol. 45, pp. 41–51.

    Article  MATH  MathSciNet  Google Scholar 

  • Dorigo, M. (1991). Ant colony optimization, new optimization techniques in engineering, by Onwubolu, G. C., and Babu B. V., Springer-Verlag Berlin Heidelberg, pp. 101–117.

  • Eskandar, H., Salehi, P., and Sabour, M. H. (2011). “Imperialist competitive ant colony algorithm for truss structures.” World Applied Sciences Journal, Vol. 12, No. 1, pp. 105–2011

    Google Scholar 

  • Geem, Z. W., Kim, J. H., and Loganathan, G. V. (2001). “A new heuristic optimization algorithm: Harmony search.” Simulation, Vol. 76, No. 2, pp. 60–8.

    Article  Google Scholar 

  • Ghasemi, M. R., Hinton, E., and Wood, R. D. (1997). “Optimization of trusses using genetic algorithms for discrete and continuous variables.” Engineering Computations, Vol. 16, pp. 272–301.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, Optimization and Machine Learning, New York.

    MATH  Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, Mich.

    Google Scholar 

  • Kaveh, A. and Abadi, A. S. M. (2011). “Harmony search based algorithms for the optimum cost design of reinforced concerete cantilever retaining walls.” International Journal of Civil Engineering, Vol. 9, No. 1, pp. 1–8.

    Google Scholar 

  • Kaveh, A. and Talatahari, S. (2009). “A particle swarm ant colony optimization for truss structures with discrete variables.” Journal of Constructional Steel Res., Vol. 65, pp. 1558–1568.

    Article  Google Scholar 

  • Kennedy, J. and Eberhart, R. (1995). “Particle swarm optimization.” In: Proc. 1995 IEEE Int. Conf. Neural Networks, Piscataway: IEEE Service Center, Vol. 4 (held in Perth), pp. 1942–1948.

    Google Scholar 

  • Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). “Optimization by simulated annealing.” Science, Vol. 220, No. 4598, pp.671–80.

    Article  MATH  MathSciNet  Google Scholar 

  • Lee, K. S., Geem, Z. W., Lee, S. H., and Bae, K. W. (2005). “The harmony search heuristic algorithm for discrete structural optimization.” Engineering Optimization, Vol. 37, No. 7, pp. 663–684.

    Article  MathSciNet  Google Scholar 

  • Lemonge, A. C. C. and Barbosa, H. J. C. (2004). “An adaptive penalty scheme for genetic algorithms in structural optimization.” International Journal for Numerical Methods in Engineering, Vol. 59, pp. 703–736.

    Article  MATH  Google Scholar 

  • Li, L. J., Huang, Z. B., and Liu, F. A. (2009). “A meta heuristic particle swarm optimization method for truss structures with discrete variables.” Computer and Structures, Vol. 87, pp. 435–443.

    Article  Google Scholar 

  • Rajeev, S. and Krishnamoorthy, C. S. (1992). “Discrete optimization of structures using genetic algorithm.” J. Struct. Eng., Vol. 118, No. 5, pp. 1233–250.

    Article  Google Scholar 

  • Rao, R. V., Savsani, V. J., and Vakharia, D. P. (2011). “Teaching-learningbased optimization: A novel method for constrained mechanical design optimization problems.” Computer-Aided Design, Vol. 4, pp. 303–315.

    Article  Google Scholar 

  • Sönmez, M. (2011). “Discrete optimum design of truss structures using artificial bee colony algorithm.” Struct. Multidisc Optim., Vol. 43, pp. 85–97.

    Article  Google Scholar 

  • Wu, S. J. and Chow, P. T. (1995). “Steady-state genetic algorithm for discrete optimization of trusses.” Computer and Structures, Vol. 56, No. 6, pp. 979–991.

    Article  MATH  Google Scholar 

  • Zhang, Y. N., Liu, J. P., Liu, B., Zhu, C. Y., and Li, Y. (2003). “Application of improved hybrid genetic algorithm to optimize.” J South China Univ. Technol., Vol. 33, No. 3, pp. 69–72.

    Google Scholar 

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Authors and Affiliations

  1. Dept. of Civil Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Tayfun Dede

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  1. Tayfun Dede
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Dede, T. Application of Teaching-Learning-Based-Optimization algorithm for the discrete optimization of truss structures. KSCE J Civ Eng 18, 1759–1767 (2014). https://doi.org/10.1007/s12205-014-0553-8

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  • DOI: https://doi.org/10.1007/s12205-014-0553-8

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