Computers and Concrete

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The effect of the new stopping criterion on the genetic algorithm performance

  • Kaya, Mustafa (Faculty of Engineering, Aksaray University) ;
  • Genc, Asim (TUSAS-Kazan Vocational School, Gazi University)
  • Received : 2020.02.16
  • Accepted : 2020.12.30
  • Published : 2021.01.25
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Abstract

In this study, a new stopping criterion, called "backward controlled stopping criterion" (BCSC), was proposed to be used in Genetic Algorithms. In the study, the available stopping citeria; adaptive stopping citerion, evolution time, fitness threshold, fitness convergence, population convergence, gene convergence, and developed stopping criterion were applied to the following four comparison problems; high strength concrete mix design, pre-stressed precast concrete beam, travelling salesman and reinforced concrete deep beam problems. When completed the analysis, the developed stopping criterion was found to be more accomplished than available criteria, and was able to research a much larger area in the space design supplying higher fitness values.

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References

  1. ACI 318-99 (1999), American Concrete Institute.
  2. Ali, M., Siarry, P. and Pant, M. (2012), "An efficient differential evaluation based algorithm for solving multi-objective optimization problems", Eur. J. Operat. Res., 217, 404-416. https://doi.org/10.1016/j.ejor.2011.09.025.
  3. Chen, L.C., Luh, C.J. and Jou, C. (2005), "Generating page clippings from web search results using a dynamically terminated genetic algorithm", Inform. Syst., 30(4), 299-316. https://doi.org/10.1016/j.is.2004.04.002.
  4. da Silva, W.R.L. and Stemberk, P. (2013), "Genetic-fuzzy approach to model concrete shrinkage", Comput. Concrete, 12(2), 109-129. https://doi.org/10.12989/cac.2013.12.2.109.
  5. Deb, K., Agarwal, S., Pratap, A. and Meyarivan, T. (2000), "A fast and elitist multi-objective genetic algorithm: NSGA-II", Technical Report 200001, IIT Kanpur, KanGAL.
  6. Deb, K., Agarwal, S., Pratap, A. and Meyarivan, T. (2000), "A fast and elitist multi-objectivegenetic algorithm: NSGA-II", Technical Report 200001, IIT Kanpur, KanGAL.
  7. Erdogan, Y.S. and Bakir, P.G. (2013), "Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem", Comput. Concrete, 11(6), 541-569. https://doi.org/10.12989/cac.2013.11.6.541.
  8. Haeser, G. and Melo, V. (2015), "Convergence detection for optimization algorithms: Approximate-KKT stopping criterion when Lagrange multipliers are not available", Operat. Res. Lett., 43(5), 484-488. https://doi.org/10.1016/j.orl.2015.06.009.
  9. Justin, Y.Q.W., Shivom, S. and Rangaiah. G.H. (2016), "Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with cutting criteria", Appl. Therm. Eng., 93, 888-899. https://doi.org/10.1016/j.applthermaleng.2015.10.055.
  10. Kaya, M. (2001), "Design of reinforced concrete deep beams using genetic algorithms", Gazi Universty Institute of Science and Technology, Ankara.
  11. Kaya, M. (2011), "The effects of a new selection operator on the performance of genetic algorithm", Appl. Math. Comput., 217, 7669-7678. https://doi.org/10.1016/j.amc.2011.02.070.
  12. Kaya, M. (2011), "The effects of two new crossover operators on genetic algorithm performance", Appl. Soft Comput., 11, 881-890. https://doi.org/10.1016/j.asoc.2010.01.008.
  13. Kaya, M. (2018), "Developing a new mutation operator to solve the RC deep beam problems by aid of genetic algorithm", Comput. Concrete, 22(5), 493-500. https://doi.org/10.12989/cac.2018.22.5.493.
  14. Kukkonen, S. and Lampinen, J. (2004), "An extension of generalized differential evolution for multi-objective optimization with constraints", International Conference on Parallel Problem Solving from Nature, Berlin, Heidelberg, 752-761.
  15. Li, H., Jiao, Y.C. and Zhang, L. (2010), "Hybrid differential evolution with a simplified quadratic approximation for constrained optimization problems", Eng. Optim., 43(2), 115-134. https://doi.org/10.1080/0305215X.2010.481021.
  16. Marti, L., Garci, J., Berlanga, A. and Molina, J.M. (2016), "A stopping criterion for multi-objective optimization evolutionary algorithms", Inform. Sci., 367-368(1), 700-718. https://doi.org/10.1016/j.ins.2016.07.025.
  17. Marti, L., Garcia, J., Berlanga, A. and Molina, J.M. (2009), "An approach to stopping criteria for multi-objective optimization, evolutionary algorithms: The MGBMcriterion", IEEE Congress on Evolutionary Computation, 1263-1270.
  18. Neuro Dimension (2014), http:/www.google.com.tr/?gws_rd=ssl#q=genetic+algorithm+cutting+criterias.
  19. Parichatprecha, R. and Nimityongskul, P. (2009), "An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks", Comput. Concrete, 6(3), 253-268. https://doi.org/10.12989/cac.2009.6.3.253.
  20. Park, W.J., Noguchi, T. and Lee, H.S. (2013), "Genetic algorithm in mix proportion design of recycled aggregate concrete. Comput. Concrete, 11(3), 183-199. https://doi.org/10.12989/cac.2013.11.3.183.
  21. Rangaiah, G.P., Sharma, S. and Lin, H.W. (2017), "Evaluation of two termination criteria in evolutionary algorithms for multiobjective optimization of complex chemical processes", Chem. Eng. Res. Des., 124, 58-65. https://doi.org/10.1016/j.cherd.2017.05.030.
  22. Rudenko, O. and Schoenauer, M. (2004), "A steady performance stopping criterion for Pareto-based evolutionary algorithm", Proceedings of the 6th Int. Multi-objective Programming and Goal Programming.
  23. Sgambi, L., Gkoumas, K. and Bontempi, F. (2014), "Genetic algorithm optimization of precast hollow core slabs", Comput. Concrete, 13(3), 389-409. https://doi.org/10.12989/cac.2014.13.3.389.
  24. Sharma, S. and Rangaiah, G.P. (2013), "An improved multiobjective differential evolution with a cutting criterion for optimizing chemical processes", Comput. Chem. Eng., 56, 155-173. https://doi.org/10.1016/j.compchemeng.2013.05.004.
  25. Sindhya, K., Deb, K. and Miettinen, K. (2008), "A local search based evolutionary multi-objective optimization approach for fast and accurate convergence", Lecture Notes in Computer Science, 815-824.
  26. Sugunthan, P.N. (2007), "Report on performance assessment of multi objective optimization algorityhms", CEC Special Session on the Performance Assessment of Real Paremeter MOEAs.
  27. Trautmann, H., Ligges, U., Mehnen, J. and Preuss, M. (2008), "A convergence criterion for multi-objective evolutionary algorithms based on systematic statistical testing", Lecture Notes in Computer Science, 825-836.
  28. Van Veldhuizen, D.A. and Lamont, G.B. (1998), "Evolutionary computation and conver-gence to a Pareto front",
  29. Wagner, T. and Trautmann, H. (2009), "Online convergence detection for evolutionary multi-objective algorithms revisited", IEEE Congress on Evolutionary Computation, 1-8.
  30. Wagner, T., Trautmann, H. and Naujoks, B. (2009), "OCD: Online convergence detection for evolutionary multi-objective algorithms based on statistical testing", Lecture Notes in Computer Science, 198-215.
  31. Wang, Y.N., Wu, L.H. and Yuan, X.F. (2010), "Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy based diversity measure", Soft Comput., 14, 193-209. https://doi.org/10.1007/s00500-008-0394-9.
  32. Webb-Robertson, B.J.M., Jarman, K.H., Harvey, S.D., Posse, C. and Wright, B.W. (2005), "An improved optimization algorithm and a Bayes factor termination criterion for sequential projection pursuit", Chemom. Intel. Lab. Syst., 77(1-2), 149-160. https://doi.org/10.1016/j.chemolab.2004.09.014
  33. Wong, J.Y., Sharma, S. and Rangaiah, G.P. (2016), "Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria", Appl. Therm. Eng., 93, 888-899. https://doi.org/10.1016/j.applthermaleng.2015.10.055.
  34. Zhang, J. and Sanderson, A.C. (2008), "Self-adaptive multiobjective differential evolution with the directional information provided by archived inferior solutions", IEEE Congress on Evolutionary Computation, 2801-2810.
  35. Zhang, Q., Zhou, A., Zhano, S., Suganthan, P.N., Liu, W. and Tiwari, S. (2009), "Multi-objective optimization test instances for the CEC 2009 special session and competition", CEC Special Session on the Performance Assessment of MultiObjective Optimization Algorithms.
  36. Zhou, G., Zhang, C., Lu, F. and Zhang, J. (2020), "Integrated optimization of cutting parameters and tool path for cavity milling considering carbon emissions", J. Clean. Prod., 250, 119454. https://doi.org/10.1016/j.jclepro.2019.119454.
  37. Zitzler, E. and Thiele, L. (1998), "Multi-objective optimization using evolutionary algorithms: A comparative case study", International Conference on Parallel Problem Solving from Nature, Berlin, Heidelberg, 292-301.
  38. Zitzler, E. and Thiele, L. (1998), "Multi-objective optimization using evolutionary algorithms: A comparative case study", International Conference on Parallel Problem Solving from Nature, Springer, Berlin, Heidelberg, 292-301.
  39. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M. and Fonseca, V.G. (2003), "Perfor-mance assessment of multi-objective optimizers: An analysis and review", IEEE Tran. Evol. Comput., 7(2), 117-132. https://doi.org/10.1109/TEVC.2003.810758.