DOI QR코드

DOI QR Code

A new hybrid optimization algorithm based on path projection

  • Received : 2017.11.18
  • Accepted : 2018.01.16
  • Published : 2018.03.25

Abstract

In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent's movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

Keywords

References

  1. Abbasnia, R., Shayanfar, M. and Khodam, A. (2014), "Reliabilitybased design optimization of structural systems using a hybrid genetic algorithm", Struct. Eng. Mech., 52, 1099-1120. https://doi.org/10.12989/sem.2014.52.6.1099
  2. AISC (2001), Manual of Steel Construction: Load and Resistance Factor Design, American Institute of Steel Construction, U.S.A.
  3. Asil Gharebaghi, S., Kaveh, A. and Ardalan Asl, M. (2017), "A new meta-heuristic optimization algorithm using star graph", Smart Struct. Syst., 20(1), 99-114. https://doi.org/10.12989/SSS.2017.20.1.099
  4. Camp, C.V., Bichon, B.J. and Stovall, S. (2005), "Design of steel frames using ant colony optimization", J. Struct. Eng., 131(3), 369-379.
  5. Cheng, M.Y. and Prayogo, D. (2014), "Symbiotic organisms search: A new metaheuristic optimization algorithm", Comput. Struct., 139, 98-112.
  6. Cheng, M.Y. and Prayogo, D. (2017), "A novel fuzzy adaptive teaching-learning-based optimization (FATLBO) for solving structural optimization problems", Eng. Comput., 33(1), 55-69. https://doi.org/10.1007/s00366-016-0456-z
  7. Cheng, M.Y., Prayogo, D., Wu, Y.W. and Lukito, M.M. (2016), "A hybrid harmony search algorithm for discrete sizing optimization of truss structure", Automat. Constr., 69, 21-33. https://doi.org/10.1016/j.autcon.2016.05.023
  8. Degertekin, S.O. (2008), "Optimum design of steel frames using harmony search algorithm", Struct. Mltidiscipl. Optim., 36(4), 393-401. https://doi.org/10.1007/s00158-007-0177-4
  9. Degertekin, S.O. (2012), "Improved harmony search algorithms for sizing optimization of truss structures", Comput. Struct., 92-93, 229-241.
  10. Degertekin, S.O. (2013), "Sizing truss structures using teachinglearning-based optimization", Comput. Struct., 119, 177-188. https://doi.org/10.1016/j.compstruc.2012.12.011
  11. Dumonteil, P. (1992), "Simple equations for effective length factors", Eng. J., 29(3), 111-115.
  12. Epitropakis, M.G., Plagianakos, V.P. and Vrahatis, M.N. (2012), "Evolving cognitive and social experience in particle swarm optimization through differential evolution: A hybrid approach", Inf. Sci., 216, 50-92. https://doi.org/10.1016/j.ins.2012.05.017
  13. Erbatur, F., Hasancebi, O., Tutuncu, I. and Kilic, H. (2000), "Optimal design of planar and space structures with genetic algorithms", Comput. Struct., 75, 209-224. https://doi.org/10.1016/S0045-7949(99)00084-X
  14. Erol, O.K. and Eksin, I. (2006), "A new optimization method: big bang-big crunch", Adv. Eng. Softw., 37(2), 106-111. https://doi.org/10.1016/j.advengsoft.2005.04.005
  15. Giftson Samuel, G. and Christober Asir Rajan, C. (2015), "Hybrid: Hybrid: Particle swarm optimization-genetic algorithm and particle swarm optimization-shuffled frog leaping algorithm for long-term generator maintenance scheduling", J. Electr. Pow. Energy Syst., 65, 432-442. https://doi.org/10.1016/j.ijepes.2014.10.042
  16. Haddar, B., Khemakhem, M., Hanafi, S. and Wilbaut, C. (2016), "A hybrid quantum particle swarm optimization for the multidimensional knapsack problem", Eng. Appl. Artif. Intell., 55, 1-13.
  17. Hanson, M.A. (1981), "On sufficiency of the kuhn-tucker conditions", J. Math. Analy. Appl., 80(2), 545-550. https://doi.org/10.1016/0022-247X(81)90123-2
  18. Hoseini, P. and Shayesteh, M.G. (2013), "Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing", Digit. Sign. Proc., 23(3), 879-893. https://doi.org/10.1016/j.dsp.2012.12.011
  19. Jaradat, G., Ayob, M. and Almarashdeh, I. (2016), "The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems", Appl. Soft Comput., 44, 45-56. https://doi.org/10.1016/j.asoc.2016.01.002
  20. Jeslin Drusila Nesamalar, J., Venkatesh, P. and Charles Raja, S. (2016), "Managing multi-line power congestion by using hybrid Nelder-Mead-Fuzzy adaptive particle swarm optimization (HNM-FAPSO)", Appl. Soft. Comput., 43, 222-234. https://doi.org/10.1016/j.asoc.2016.02.013
  21. Kaveh, A. and Ilchi Ghazaan, M. (2015), "A comparative study of CBO and ECBO for optimal design of skeletal structures", Comput. Struct., 153, 137-147. https://doi.org/10.1016/j.compstruc.2015.02.028
  22. Kaveh, A. and Ilchi Ghazaan, M. (2018), "A new hybrid metaheuristic algorithm for optimal design of large-scale dome structures", Eng. Optim., 50(2), 235-252. https://doi.org/10.1080/0305215X.2017.1313250
  23. Kaveh, A. and Javadi, S.M. (2014), "An efficient hybrid particle swarm strategy, ray optimizer, and harmony search algorithm for optimal design of truss structures", Period. Polytech., 58(2), 65-81.
  24. Kaveh, A. and Laknejadi, K. (2013), "A hybrid evolutionary graph based multi-objective algorithm for layout optimization of truss structures", Acta Mech., 224, 343-364. https://doi.org/10.1007/s00707-012-0754-5
  25. Kaveh, A. and Mahdavi, V.R. (2013), "Optimal design of structures with multiple natural frequency constraints using a hybridized BB-BC/Quasi-Newton algorithm", Period. Politech., 57(1), 1-12.
  26. Kaveh, A. and Shahrouzi, M. (2008), "Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization", J. Numer. Meth. Eng., 73(4), 544-563. https://doi.org/10.1002/nme.2088
  27. Kaveh, A. and Talatahari, S. (2009a), "Hybrid algorithm of harmony search, particle swarm and ant colony for structural design optimization", Stud. Comput. Intellig., 239, 159-198.
  28. Kaveh, A. and Talatahari, S. (2009b), "Size optimization of space trusses using big bang-big crunch algorithm", Comput. Struct., 87, 1129-1140.
  29. Kaveh, A. and Talatahari, S. (2009c), "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures", Comput. Struct., 87(5), 267-283. https://doi.org/10.1016/j.compstruc.2009.01.003
  30. Kaveh, A. and Talatahari, S. (2010a), "A discrete big bang-big crunch algorithm for optimal design of skeletal structures", Asian J. Civil Eng., 11(1), 103-122.
  31. 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
  32. Kaveh, A. and Talatahari, S. (2012), "Charged system search for optimal design of frame structures", Appl. Soft Comput., 12(1), 382-393. https://doi.org/10.1016/j.asoc.2011.08.034
  33. Kaveh, A., Bakhshpoori, T. and Afshari, E. (2015), "Hybrid PSO and SSO algorithm for truss layout and size optimization considering dynamic constraints", Struct. Eng. Mech., 54(3), 453-474. https://doi.org/10.12989/sem.2015.54.3.453
  34. Kaveh, A., Sheikholeslami, R., Talatahari, S. and Keshvari-Ilkhichi, M. (2014), "Chaotic swarming of particles: A new method for size optimization of truss structures", Adv. Eng. Softw., 67, 136-147. https://doi.org/10.1016/j.advengsoft.2013.09.006
  35. Kelner, V., Capitanescu, F., Leonard, O. and Wehenkel, L. (2008), "A hybrid optimization technique coupling an evolutionary and a local search algorithm", J. Comput. Appl. Math., 215(2), 448-456. https://doi.org/10.1016/j.cam.2006.03.048
  36. Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983), "Optimization by simulated annealing", Sci., 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671
  37. Li, L.J., Huang, Z.B. and Liu, F. (2009), "A heuristic particle swarm optimization method for truss structures with discrete variables", Comput. Struct., 87(7), 435-443. https://doi.org/10.1016/j.compstruc.2009.01.004
  38. Liu, J., Zhang, S., Wu, C., Liang, J., Wang, X. and Teo, K.L. (2016), "A hybrid approach to constrained global optimization", Appl. Soft Comput., 47, 281-294. https://doi.org/10.1016/j.asoc.2016.05.021
  39. Luenberger, D.G. (1973), Introduction to Linear and Nonlinear Programming, Addison-Wesley, Reading, Mass.
  40. Ma, H., Simon, D., Fei, M., Shu, X. and Chen, Z. (2014), "Hybrid biogeography-based evolutionary algorithms", Eng. Appl. Artif. Intell., 30, 213-224. https://doi.org/10.1016/j.engappai.2014.01.011
  41. Nayanatara, C., Baskaran, J. and Kothari, D.P. (2016), "Hybrid optimization implemented for distributed generation parameters in a power system network", J. Electr. Pow. Energy Syst., 78, 690-699.
  42. Ouyang, H., Gao, L., Kong X., Li, S. and Zou, D. (2016), "Hybrid harmony search particle swarm optimization with global dimension selection", Inf. Sci., 346, 318-337.
  43. Perez, R.E. and Behdinan, K. (2007), "Particle swarm approach for structural design optimization", Comput. Struct., 85, 1579-1588. https://doi.org/10.1016/j.compstruc.2006.10.013
  44. Prayogo, D., Cheng, M.Y., Wu, Y.W., Herdany, A.A. and Prayogo, H. (2018), "Differential big bang-big crunch algorithm for construction-engineering design optimization", Automat. Constr., 85, 290-304. https://doi.org/10.1016/j.autcon.2017.10.019
  45. Rahami, H., Kaveh, A., Aslani, M. and Najian Asl, R. (2011), "A hybrid modified genetic-nelder mead simplex algorithm for large-scale truss optimization", J. Optim. Civil Eng., 1(1), 29-46.
  46. Rosen, J.B. (1960), "The gradient projection method for nonlinear programming. Part I. Linear constraints", J. Soc. Industr. Appl. Math., 8(1), 181-217. https://doi.org/10.1137/0108011
  47. Rosen, J.B. (1961), "The gradient projection method for nonlinear programming. Part II. Nonlinear constraints", J. Soc. Industr. Appl. Math., 9(4), 514-532. https://doi.org/10.1137/0109044
  48. Saka, M.P. and Kameshki, E.S. (1998), "Optimum design of multistory sway steel frames to bs5950 using genetic algorithm", Proceedings of the 4th International Conference on Computational Structures Technology, Edinburgh, Scotland, U.K.
  49. Shao, W., Pi, D. and Shao, Z. (2016), "A hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism for no-wait flow shop scheduling", Know-Bas. Syst., 107, 219-234. https://doi.org/10.1016/j.knosys.2016.06.011
  50. Soleimani, H. and Kannan, G. (2015), "A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks", Appl. Math. Modell., 39(14), 3990-4012. https://doi.org/10.1016/j.apm.2014.12.016
  51. Talatahari, S. (2016), "Symbiotic organisms search for optimum design of and grillage system", Asian J. Civil Eng., 17(3), 299-313.
  52. 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
  53. Tuba, M. and Bacanin, N. (2014), "Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems", Neurocomput., 143, 197-207.
  54. Wang, H., Sun, H., Li, C., Rahnamayan, S. and Pan, J.S. (2013), "Diversity enhanced particle swarm optimization with neighborhood search", Inf. Sci., 223, 119-135. https://doi.org/10.1016/j.ins.2012.10.012
  55. Wang, J., Yuan, W. and Cheng, D. (2015), "Hybrid genetic-particle swarm algorithm: An efficient method for fast optimization of atomic clusters", Comput. Theoret. Chem., 1059, 12-17. https://doi.org/10.1016/j.comptc.2015.02.003
  56. Wu, J., Long, J. and Liu, M. (2015), "Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm", Neurocomput., 148, 136-142. https://doi.org/10.1016/j.neucom.2012.10.043