Structural Engineering and Mechanics

  • 제77권5호
  • /
  • Pages.613-626
  • /
  • 2021
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Optimization of structural and mechanical engineering problems using the enriched ViS-BLAST method

  • 투고 : 2020.10.21
  • 심사 : 2020.12.22
  • 발행 : 2021.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, an enhanced Violation-based Sensitivity analysis and Border-Line Adaptive Sliding Technique (ViS-BLAST) will be utilized for optimization of some well-known structural and mechanical engineering problems. ViS-BLAST has already been introduced by the authors for solving truss optimization problems. For those problems, this method showed a satisfactory enactment both in speed and efficiency. The Enriched ViS-BLAST or EVB is introduced to be vastly applicable to any solvable constrained optimization problem without any specific initialization. It uses one-directional step-wise searching technique and mostly limits exploration to the vicinity of FNF border and does not explore the entire design space. It first enters the feasible region very quickly and keeps the feasibility of solutions. For doing this important, EVB groups variables for specifying the desired searching directions in order to moving toward best solutions out or inside feasible domains. EVB was employed for solving seven numerical engineering design problems. Results show that for problems with tiny or even complex feasible regions with a larger number of highly non-linear constraints, EVB has a better performance compared to some records in the literature. This dominance was evaluated in terms of the feasibility of solutions, the quality of optimum objective values found and the total number of function evaluations performed.

키워드

참고문헌

  1. Akbulut, M., Sarac, A. and Ertas, A.H. (2020), "An investigation of non-linear optimization methods on composite structures under vibration and buckling loads", Adv. Comput. Des., 5(3), 209-231. http://dx.doi.org/10.12989/acd.2020.5.3.209.
  2. Alatas, B. (2011), "ACROA: artificial chemical reaction optimization algorithm for global optimization", Exp. Syst. Appl., 38(10), 13170-13180. https://doi.org/10.1016/j.eswa.2011.04.126.
  3. Alatas, B. (2012), "A novel chemistry based metaheuristic optimization method for mining of classification rules", Exp. Syst. Appl., 39(12), 11080-11088. http://dx.doi.org/10.1016/j.eswa.2012.03.066.
  4. Arora, J. (2004), Introduction to Optimum Design, Academic Press, San Diego, California, USA.
  5. Askarzadeh, A. (2016), "A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm", Comput. Struct., 169, 1-12. http://dx.doi.org/10.1016/j.compstruc.2016.03.001.
  6. Atashpaz-Gargari, E. and Lucas, C. (2007), "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", IEEE Congress on Evolutionary Computation, 4661-4667. https://doi.org/10.1109/CEC.2007.4425083.
  7. Banh, T.T., Shin, S. and Lee, D. (2018), "Topology optimization for thin plate on elastic foundations by using multi-material", Steel Compos. Struct., 27(2), 177-184. http://dx.doi.org/10.12989/scs.2018.27.2.177.
  8. Becerra, R.L. and Coello, C.A.C. (2006), "Cultured differential evolution for constrained optimization", Comput. Meth. Appl. Mech. Eng., 195(33), 4303-4322. https://doi.org/10.1016/j.cma.2005.09.006.
  9. Belegundu, A.D. and Chandrupatla, T.R. (2011), Optimization Concepts and Applications in Engineering, Cambridge University Press, New York, USA.
  10. Blum, C. and Roli, A. (2003), "Metaheuristics in combinatorial optimization: Overview and conceptual comparison", ACM Comput. Survey. (CSUR), 35(3), 268-308. https://doi.org/10.1145/937503.937505.
  11. Cheng, M.-Y. and Prayogo, D. (2014), "Symbiotic organisms search: a new metaheuristic optimization algorithm", Comput. Struct., 139, 98-112. https://doi.org/10.1016/j.compstruc.2014.03.007.
  12. Chootinan, P. and Chen, A. (2006), "Constraint handling in genetic algorithms using a gradient-based repair method", Comput. Operat. Res., 33(8), 2263-2281. https://doi.org/10.1016/j.cor.2005.02.002.
  13. Coello Coello, C.A. (2000), "Constraint-handling using an evolutionary multiobjective optimization technique", Civil Eng. Syst., 17(4), 319-346. https://doi.org/10.1080/02630250008970288.
  14. Coello Coello, C.A. and Becerra, R.L. (2004), "Efficient evolutionary optimization through the use of a cultural algorithm", Eng. Optim., 36(2), 219-236. https://doi.org/10.1080/03052150410001647966.
  15. Coello, C.A.C. (2000), "Use of a self-adaptive penalty approach for engineering optimization problems", Comput. Indus., 41(2), 113-127. https://doi.org/10.1016/S0166-3615(99)00046-9.
  16. Coello, C.A.C. and Montes, E.M. (2002), "Constraint-handling in genetic algorithms through the use of dominance-based tournament selection", Adv. Eng. Inform., 16(3), 193-203. https://doi.org/10.1016/S1474-0346(02)00011-3.
  17. Cuevas, E., Cienfuegos, M., Zaldivar, D. and Perez-Cisneros, M. (2013), "A swarm optimization algorithm inspired in the behavior of the social-spider", Exp. Syst. Appl., 40(16), 6374-6384. https://doi.org/10.1016/j.eswa.2013.05.041.
  18. Deb, K. (1991), "Optimal design of a welded beam via genetic algorithms", AIAA J., 29(11), 2013-2015. https://doi.org/10.2514/3.10834.
  19. Dizangian, B. and Ghasemi, M. (2015a), "A fast marginal feasibility search method in size optimization of truss structures", Asian J. Civil Eng. (BHRC), 16(5), 567-585.
  20. Dizangian, B. and Ghasemi, M.R. (2015b), "Ranked-based sensitivity analysis for size optimization of structures", J. Mech. Des., 137(12), 121402. https://doi.org/10.1115/1.4031295.
  21. Dizangian, B. and Ghasemi, M.R. (2016a), "A fast decoupled reliability-based design optimization of structures using B-spline interpolation curves", J. Brazil. Soc. Mech. Sci. Eng., 38(6), 1817-1829. https://doi.org/10.1007/s40430-015-0423-4.
  22. Dizangian, B. and Ghasemi, M.R. (2016b), "An efficient method for reliable optimum design of trusses", Steel Compos. Struct., 21(5), 1069-1084. http://dx.doi.org/10.12989/scs.2016.21.5.1069.
  23. Dorigo, M., Maniezzo, V. and Colorni, A. (1996), "Ant system: optimization by a colony of cooperating agents", Syst. Man Cybernet., Part B: Cybernet., IEEE Tran., 26(1), 29-41. https://doi.org/10.1109/3477.484436.
  24. dos Santos Coelho, L. (2010), "Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems", Exp. Syst. Appl., 37(2), 1676-1683. https://doi.org/10.1016/j.eswa.2009.06.044.
  25. Eita, M. and Fahmy, M. (2010), "Group counseling optimization: a novel approach", Research and Development in Intelligent Systems XXVI, Springer, London, UK.
  26. 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.
  27. Eskandar, H., Sadollah, A., Bahreininejad, A. and Hamdi, M. (2012), "Water cycle algorithm-A novel metaheuristic optimization method for solving constrained engineering optimization problems", Comput. Struct., 110, 151-166. https://doi.org/10.1016/j.compstruc.2012.07.010.
  28. Gandomi, A.H. and Alavi, A.H. (2012), "Krill herd: a new bioinspired optimization algorithm", Commun. Nonlin. Sci. Numer. Simul., 17(12), 4831-4845. https://doi.org/10.1016/j.cnsns.2012.05.010.
  29. Gandomi, A.H., Yang, X.S. and Alavi, A.H. (2013), "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems", Eng. Comput., 29, 17-35. http://dx.doi.org/10.1007/s00366-012-0308-4.
  30. Ghasemi, M.R. and Varaee, H. (2017b), "A fast multi-objective optimization using an efficient ideal gas molecular movement algorithm", Eng. Comput., 33(3), 477-496. https://doi.org/10.1007/s00366-016-0485-7.
  31. Gold, S. and Krishnamurty, S. (1997), "Tradeoffs in robust engineering design", Proceeding of 1997 ASME Design Engineering Technical Conferences, Sacramento, CA, USA.
  32. Gupta, S., Tiwari, R. and Nair, S.B. (2007), "Multi-objective design optimisation of rolling bearings using genetic algorithms", Mech. Mach. Theor., 42(10), 1418-1443. https://doi.org/10.1016/j.mechmachtheory.2006.10.002.
  33. Holland, J.H. (1992), Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, And Artificial Intelligence, MIT Press, Cambridge, MA, United States.
  34. Hsu, Y.L. and Liu, T.C. (2007), "Developing a fuzzy proportional-derivative controller optimization engine for engineering design optimization problems", Eng. Optim., 39(6), 679-700. https://doi.org/10.1080/03052150701252664.
  35. Kamkar, I., Akbarzadeh-T, M.R. and Yaghoobi, M. (2010), "Intelligent water drops a new optimization algorithm for solving the vehicle routing problem", Systems Man and Cybernetics (SMC), IEEE International Conference on, Istanbul, Turkey, October.
  36. Kashan, A.H. (2014), "League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships", Appl. Soft Comput., 16, 171-200. https://doi.org/10.1016/j.asoc.2013.12.005.
  37. Kaveh, A. (2014), Advances in Metaheuristic Algorithms for Optimal Design of Structures, Springer International Publishing, Gewerbestrasse, Switzerland.
  38. Kaveh, A. and Khayatazad, M. (2012), "A new meta-heuristic method: ray optimization", Comput. Struct., 112, 283-294. https://doi.org/10.1016/j.compstruc.2012.09.003.
  39. Kaveh, A. and Mahdavi, V. (2014), "Colliding bodies optimization: a novel meta-heuristic method", Comput. Struct., 139, 18-27. https://doi.org/10.1016/j.compstruc.2014.04.005.
  40. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Neural Networks, Proceedings., IEEE International Conference on, 1942-1948, Perth, WA, Australia November.
  41. Krohling, R.A. and dos Santos Coelho, L. (2006), "Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems", IEEE Tran. Syst. Man Cybernet., Part B (Cybernet.), 36(6), 1407-1416. https://doi.org/10.1109/TSMCB.2006.873185
  42. Kumar, S., Tejani, G.G. and Mirjalili, S. (2019), "Modified symbiotic organisms search for structural optimization", Eng. Comput., 35(4), 1269-1296. https://doi.org/10.1007/s00366- 018-0662-y.
  43. Kumar, S., Tejani, G.G., Pholdee, N. and Bureerat, S. (2020), "Multi-objective modified heat transfer search for truss optimization", Eng. Comput., 1-16. https://doi.org/10.1007/s00366-020-01010-1.
  44. Lampinen, J. (2002), "A constraint handling approach for the differential evolution algorithm", Proceedings of the 2002 Congress on Evolutionary Computation, CEC'02 (Cat. No. 02TH8600), 1468-1473, Honolulu, May.
  45. Lee, D., Shin, S. and Doan, Q.H. (2018), "Real-time robust assessment of angles and positions of nonscaled steel outrigger structure with Maxwell-Mohr method", Constr. Build. Mater., 186, 1161-1176. https://doi.org/10.1016/j.conbuildmat.2018.07.212.
  46. Li, H., Kafka, O.L., Gao, J., Yu, C., Nie, Y., Zhang, L., Tajdari, M., Tang, S., Guo, X. and Li, G. (2019), "Clustering discretization methods for generation of material performance databases in machine learning and design optimization", Comput. Mech., 64(2), 281-305. https://doi.org/10.1007/s00466-019-01716-0.
  47. Liu, H., Cai, Z. and Wang, Y. (2010), "Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization", Appl. Soft Comput., 10(2), 629-640. https://doi.org/10.1016/j.asoc.2009.08.031.
  48. Madenci, E. and Gulcu, S. (2020), "Optimization of flexure stiffness of FGM beams via artificial neural networks by mixed FEM", Struct. Eng. Mech., 75(5), 633-642. http://dx.doi.org/10.12989/sem.2020.75.5.633.
  49. Mahdavi, M., Fesanghary, M. and Damangir, E. (2007), "An improved harmony search algorithm for solving optimization problems", Appl. Math. Comput., 188(2), 1567-1579. https://doi.org/10.1016/j.amc.2006.11.033.
  50. Michalewicz, Z. (1995), "Genetic algorithms, numerical optimization, and constraints", Proceedings of The Sixth International Conference on Genetic Algorithms, 195, 151-158, Morgan Kauffman San Mateo.
  51. Michalewicz, Z. and Attia, N. (1994), "Evolutionary optimization of constrained problems", Proceedings of the 3rd Annual Conference on Evolutionary Programming, Singapore.
  52. Mirjalili, S. and Lewis, A. (2016), "The whale optimization algorithm", Adv. Eng. Softw., 95, 51-67. http://dx.doi.org/10.1016/j.advengsoft.2016.01.008.
  53. Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey wolf optimizer", Adv. Eng. Softw., 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
  54. Mucherino, A. and Seref, O. (2007), "Monkey search: a novel metaheuristic search for global optimization", AIP Conf. Proc., 953(1), 162-173. https://doi.org/10.1063/1.2817338.
  55. Naderi, A., Sohrabi, M.R., Ghasemi, M.R. and Dizangian, B. (2020), "Total and partial updating technique: A swift approach for cross-section and geometry optimization of truss structures", KSCE J. Civil Eng., 24, 1219-1227. https://doi.org/10.1007/s12205-020-2096-5.
  56. Phukaokaew, W., Sleesongsom, S., Panagant, N. and Bureerat, S. (2019), "Synthesis of four-bar linkage motion generation using optimization algorithms", Adv. Comput. Des., 4(3), 197-210. http://dx.doi.org/10.12989/acd.2019.4.3.197.
  57. Rao, B.R. and Tiwari, R. (2007), "Optimum design of rolling element bearings using genetic algorithms", Mech. Mach. Theor., 42(2), 233-250. https://doi.org/10.1016/j.mechmachtheory.2006.02.004.
  58. Rao, R.V., Savsani, V.J. and Vakharia, D. (2011), "Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems", Comput. Aid. Des., 43(3), 303-315. https://doi.org/10.1016/j.cad.2010.12.015.
  59. Rao, S.S. and Rao, S.S. (2009), Engineering Optimization: Theory and Practice, John Wiley & Sons, Hoboken, USA.
  60. Rashedi, E., Nezamabadi-Pour, H. and Saryazdi, S. (2009), "GSA: a gravitational search algorithm", Inform. Sci., 179(13), 2232-2248. https://doi.org/10.1016/j.ins.2009.03.004.
  61. Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M. (2013), "Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems", Appl. Soft Comput., 13(5), 2592-2612. http://dx.doi.org/10.1016/j.asoc.2012.11.026.
  62. Savsani, V.J., Tejani, G.G. and Patel, V.K. (2016), "Truss topology optimization with static and dynamic constraints using modified subpopulation teaching-learning-based optimization", Eng. Optim., 48(11), 1990-2006. https://doi.org/10.1080/0305215X.2016.1150468.
  63. Shah-Hosseini, H. (2011), "Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation", Int. J. Comput. Sci. Eng., 6(2), 132-140. https://dx.doi.org/10.1504/IJCSE.2011.041221.
  64. Taheri, F., Ghasemi, M.R. and Dizangian, B. (2020), "Practical optimization of power transmission towers using the RBF-based ABC algorithm", Struct. Eng. Mech., 73(4), 463-479. http://dx.doi.org/10.12989/sem.2020.73.4.463.
  65. Takahama, T. and Sakai, S. (2005), "Constrained optimization by applying the/spl alpha/constrained method to the nonlinear simplex method with mutations", IEEE Tran. Evol. Comput., 9(5), 437-451. https://doi.org/10.1109/TEVC.2005.850256.
  66. Tamura, K. and Yasuda, K. (2011), "Spiral optimization-A new multipoint search method", 2011 IEEE International Conference on Systems, Man, and Cybernetics, October.
  67. Tamura, K. and Yasuda, K. (2011), "Spiral optimization -A new multipoint search method", IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK.
  68. Tejani, G.G., Kumar, S. and Gandomi, A.H. (2019b), "Multi-objective heat transfer search algorithm for truss optimization", Eng. Comput., 1-22. https://doi.org/10.1007/s00366-019-00846-6.
  69. Tejani, G.G., Savsani, V.J. and Patel, V.K. (2016), "Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization", J. Comput. Des. Eng., 3(3), 226-249. https://doi.org/10.1016/j.jcde.2016.02.003.
  70. Tejani, G.G., Savsani, V.J., Bureerat, S. and Patel, V.K. (2018b), "Topology and size optimization of trusses with static and dynamic bounds by modified symbiotic organisms search", J. Comput. Civil Eng., 32(2), 04017085. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000741.
  71. Tejani, G.G., Savsani, V.J., Bureerat, S., Patel, V.K. and Savsani, P. (2019a), "Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms", Eng. Comput., 35(2), 499-517. https://doi.org/10.1007/s00366-018-0612-8.
  72. Tejani, G.G., Savsani, V.J., Patel, V.K. and Savsani, P.V. (2018a), "Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics", J. Comput. Des. Eng., 5(2), 198-214. https://doi.org/10.1016/j.jcde.2017.10.001.
  73. Varaee, H. and Ghasemi, M.R. (2017), "Engineering optimization based on ideal gas molecular movement algorithm", Eng. Comput., 33(1), 71-93. https://doi.org/10.1007/s00366-016-0457-y.
  74. Wang, G.G. (2003), "Adaptive response surface method using inherited latin hypercube design points", Tran.-Am. Soc. Mech. Eng. J. Mech. Des., 125(2), 210-220. https://doi.org/10.1115/1.1561044.
  75. Wang, L. and Li, L.P. (2010), "An effective differential evolution with level comparison for constrained engineering design", Struct. Multidisc. Optim., 41(6), 947-963. https://doi.org/10.1007/s00158-009-0454-5.
  76. Yang, X.S. (2010a), Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome, United Kingdom.
  77. Yang, X.S. (2010b), Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, Hoboken, New Jersey, USA.
  78. Zhang, M., Luo, W. and Wang, X. (2008), "Differential evolution with dynamic stochastic selection for constrained optimization", Inform. Sci., 178(15), 3043-3074. https://doi.org/10.1016/j.ins.2008.02.014.