Browse > Article
http://dx.doi.org/10.12989/sem.2021.77.5.613

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

Dizangian, Babak (Department of Civil Engineering, Velayat University)
Ghasemi, Mohammad Reza (Department of Civil Engineering, University of Sistan and Baluchestan)
Publication Information
Structural Engineering and Mechanics / v.77, no.5, 2021 , pp. 613-626 More about this Journal
Abstract
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.
Keywords
constrained optimization; sensitivity analysis; mechanical engineering design; highly non-linear;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.   DOI
2 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.   DOI
3 Gold, S. and Krishnamurty, S. (1997), "Tradeoffs in robust engineering design", Proceeding of 1997 ASME Design Engineering Technical Conferences, Sacramento, CA, USA.
4 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.   DOI
5 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.
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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.
10 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.   DOI
11 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.   DOI
12 Kaveh, A. (2014), Advances in Metaheuristic Algorithms for Optimal Design of Structures, Springer International Publishing, Gewerbestrasse, Switzerland.
13 Yang, X.S. (2010b), Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, Hoboken, New Jersey, USA.
14 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.   DOI
15 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.   DOI
16 Yang, X.S. (2010a), Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome, United Kingdom.
17 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.   DOI
18 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.   DOI
19 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.   DOI
20 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.   DOI
21 Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Neural Networks, Proceedings., IEEE International Conference on, 1942-1948, Perth, WA, Australia November.
22 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.   DOI
23 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   DOI
24 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.   DOI
25 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.   DOI
26 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.   DOI
27 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.   DOI
28 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.   DOI
29 Rao, S.S. and Rao, S.S. (2009), Engineering Optimization: Theory and Practice, John Wiley & Sons, Hoboken, USA.
30 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.   DOI
31 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.   DOI
32 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.   DOI
33 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.   DOI
34 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.   DOI
35 Tamura, K. and Yasuda, K. (2011), "Spiral optimization-A new multipoint search method", 2011 IEEE International Conference on Systems, Man, and Cybernetics, October.
36 Tamura, K. and Yasuda, K. (2011), "Spiral optimization -A new multipoint search method", IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK.
37 Arora, J. (2004), Introduction to Optimum Design, Academic Press, San Diego, California, USA.
38 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.   DOI
39 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.   DOI
40 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.   DOI
41 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.
42 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.   DOI
43 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.   DOI
44 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.   DOI
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.   DOI
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.   DOI
47 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.   DOI
48 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.   DOI
49 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.   DOI
50 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.   DOI
51 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.   DOI
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.   DOI
53 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.   DOI
54 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.   DOI
55 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.   DOI
56 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.
57 Michalewicz, Z. and Attia, N. (1994), "Evolutionary optimization of constrained problems", Proceedings of the 3rd Annual Conference on Evolutionary Programming, Singapore.
58 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.   DOI
59 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.   DOI
60 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.
61 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.   DOI
62 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.   DOI
63 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.   DOI
64 Eita, M. and Fahmy, M. (2010), "Group counseling optimization: a novel approach", Research and Development in Intelligent Systems XXVI, Springer, London, UK.
65 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.   DOI
66 Belegundu, A.D. and Chandrupatla, T.R. (2011), Optimization Concepts and Applications in Engineering, Cambridge University Press, New York, USA.
67 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.   DOI
68 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.   DOI
69 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.   DOI
70 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.   DOI
71 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.   DOI
72 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.   DOI
73 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.   DOI
74 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.   DOI
75 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.   DOI
76 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.   DOI
77 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.   DOI
78 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.   DOI