Computers and Concrete

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Developing a new mutation operator to solve the RC deep beam problems by aid of genetic algorithm

  • Received : 2017.06.23
  • Accepted : 2018.11.29
  • Published : 2018.11.25
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Abstract

Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.

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References

  1. ACI 318-99 (1999), American Concrete Institute.
  2. Albayrak, M. and Allahverdi, N. (2011), "Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms", Exp. Syst. Appl., 38, 1313-1320. https://doi.org/10.1016/j.eswa.2010.07.006
  3. Berry, A. and Vamplew, P. (2004), "PhD can mutate: a simple dynamic directed mutation approach for genetic algorithms", Proceedings of International Conference on Artificial Intelligence in Science and Technology, 200-205.
  4. Castilho, V.C., Mounir, M.K.D. and Nicoletti, M.C (2007), "Using a modified genetic algorithm to minimize the production costs for slabs of precast pre-stressed concrete joints", Constr. Build. Mater., 85, 78-90.
  5. Daoud, M.I. and Kharma, N. (2011), "A hybrid heuristic-genetic algorithm for task scheduling in heterogeneous processor networks", J. Parallel Distrib. Comput., 71, 1518-1531. https://doi.org/10.1016/j.jpdc.2011.05.005
  6. Deep, K. and Thakur, M. (2007), "A new crossover operator for real coded genetic algorithms", Appl. Math. Comput., 188(1), 895-911. https://doi.org/10.1016/j.amc.2006.10.047
  7. Goldberg, D.E. (1989), Genetic Algorithms in Search. Optimization And Machine Learning. Reading, Addison-Wesley, MA.
  8. Govindaraj, V. and Ramasamy, J.V. (2005), "Optimum detailed design of reinforced concrete continuous beams using genetic algorithms", Comput. Struct., 84(1-2), 34-48. https://doi.org/10.1016/j.compstruc.2005.09.001
  9. Gupta, S., Agarwal, G. and Kumar, V. (2010), "Task scheduling in multiprocessor system using genetic algorithm, Machine Learning and Computing (ICMLC)", 2010 Second International Conference on IEEE, 267-271.
  10. Hansen, N. and Ostermeier, A. (2001), "Completely derandomized self adaptation in evolutionary strategies", IEEE Tran. Evol. Comput., 9(2), 159-195. https://doi.org/10.1162/106365601750190398
  11. Heitzunger, C. and Selberherr, S. (2002), "An extensible TCAD optimization framework combing gradient based and genetic optimizers", Microelectron. J., 33, 61-68. https://doi.org/10.1016/S0026-2692(01)00105-7
  12. Holland, J.H. (1975), Adaptation in Natural And Artificial Systems, The University of Michigan Press, Ann Arbor.
  13. Hwang, R., Gen, M. and Katayama, H. (2006), "A performance evaluation of multiprocessor scheduling with genetic algorithm", Asia Pacif. Manage. Rev., 11(2), 67-72..
  14. Kaya, M. (2011), "The effects of a new selection operator on the performance of genetic algorithm", Appl. Math. Comput., 217, 7669-7678.
  15. Kaya, M. (2011), "The effects of two new crossover operators on genetic algorithm performance", Appl. Sof. Comput., 11, 881-890. https://doi.org/10.1016/j.asoc.2010.01.008
  16. Kolodziej, J. and Khan, S.U. (2012), "Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment", Inf. Sci., 214, 1-19. https://doi.org/10.1016/j.ins.2012.05.016
  17. Korejo, I., Yang, S. and Li, C. (2010), "A directed mutation operator for real coded genetic algorithms, Evo applications, Part I", LNCS 6024, 491-500.
  18. Lobo, F., Lima, C. and Michalewicz, Z. (2007), "Parameter setting in evolutionary algorithms", Stud. Comput. Intel., 54, 47-76.
  19. Lu, H., Niu, R., Liu, J. and Zhu, Z. (2013), "A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem", Appl. Soft Comput., 13, 2790-2802. https://doi.org/10.1016/j.asoc.2012.10.001
  20. Nilson, A.D.D. (1997), Design of Concrete Structures, The McGraw Hill CoMpanies, Inc.
  21. Omara, F.A. and Arafa, M.M. (2010), "Genetic algorithms for task scheduling problem", J. Parallel Distrib. Comput., 70, 13-22. https://doi.org/10.1016/j.jpdc.2009.09.009
  22. Pratihar, D.K. (2008), Soft Computing, Alpha Science International Ltd., Oxford, UK, 2008.
  23. Rahmani, A.M. and Vahedi, M.A. (2008), "A novel task scheduling in multiprocessor systems with genetic algorithm by using Elitism stepping method", J. Comput. Theor. Eng., 1(1), 1. https://doi.org/10.4086/toc.gs.2008.001
  24. Sahab, M.G., Ashour, A.F. and Toropov, V.V. (1999), "A hybrid genetic algorithm for reinforced concrete flat slab buildings", Comput Struct., 83(8-9), 551-559. https://doi.org/10.1016/j.compstruc.2004.10.013
  25. Sathappan, O., Chitra, P., Venkatesh, P. and Prabhu, M. (2011), "Modified genetic algorithm for multiobjective task scheduling on heterogeneous computing system", Int. J. Inf. Technol. Commun. Converg., 1, 146-158.
  26. Singh, J. and Singh, G. (2012), "Improved task scheduling on parallel system using genetic algorithm", Int. J. Comput. Appl., 39, 17-22.
  27. Zhou, Q. and Li, Y. (2003), "Directed variation in evolutionary strategies", IEEE Tran. Evol. Comput., 7(4), 356-366. https://doi.org/10.1109/TEVC.2003.812215

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