Enhanced teacher-learning based algorithm in real size structural optimization
Space frame structures that are made up of a huge number of members are often used on a large scale, hence their accurate evaluation is important to achieve the optimal design. On the other hand, the use of space Frames and 3D truss structures has become more popular due to its time efficiency. Also, these types of structures can carry loads in longspan buildings and are used in large-scale structures such as halls, hangars, passenger stations, etc. In this study, a novel evolutionary algorithm, named ETLBO, has been proposed for the optimization of space frame design in real-size structures. Despite the existing methods in the literature, the ETLBO method can be used for large-scale space frame structures due to its high speed with sufficient accuracy. At first, four optimization algorithms Particle swarm optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Teaching–learning-based optimization (TLBO) under structural problems have been evaluated. The results show that the TLBO algorithm performs better in solving problems and has been better in most problems than other algorithms. So, we have tried to improve this algorithm based on a machine learning approach and combination operators. Algorithm improvement is created by adding a crossover operation between the new solution and the best solution in the teacher phase. This change causes a sudden movement and escapes from the local minima for the algorithm. Enhanced algorithm results show that convergence speed and optimal response quality have improved. Finally, using this algorithm, several new practical examples have been optimized.
Keyword : large-scale structure, space frame structures, optimization, hybrid method
This work is licensed under a Creative Commons Attribution 4.0 International License.
American Institute of Steel Construction. (1989). Manual of steel construction. Allowable stress design (9th ed.). Chicago, Illinois.
Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation (pp. 4661–4667). IEEE. https://doi.org/10.1109/CEC.2007.4425083
Aye, C. M., Pholdee, N., Yildiz, A. R., Bureerat, S., & Sait, S. M. (2019). Multi-surrogate-assisted metaheuristics for crashworthiness optimisation. International Journal of Vehicle Design, 80(2–4), 223–240. https://doi.org/10.1504/IJVD.2019.109866
Champasak, P., Panagant, N., Pholdee, N., Bureerat, S., & Yildiz, A. R. (2020). Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle. Aerospace Science and Technology, 100, 105783. https://doi.org/10.1016/j.ast.2020.105783
Cheng, M.-Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112. https://doi.org/10.1016/j.compstruc.2014.03.007
Dhiman, G., Singh, K. K., Slowik, A., Chang, V., Yildiz, A. R., Kaur, A., & Garg, M. (2021). EMoSOA: A new evolutionary multi-objective seagull optimization algorithm for global optimization. International Journal of Machine Learning and Cybernetics, 12(2), 571–596. https://doi.org/10.1007/s13042-020-01189-1
Demirci, E., & Yıldız, A. R. (2019). A new hybrid approach for reliability-based design optimization of structural components. Materials Testing, 61(2), 111–119. https://doi.org/10.3139/120.111291
Es-Haghi, M. S., Shishegaran, A., & Rabczuk, T. (2020). Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel frames. Frontiers of Structural and Civil Engineering, 14(5), 1110–1130. https://doi.org/10.1007/s11709-020-0643-2
Eskandar, H., Salehi, P., & Sabour, M. H. (2011). Imperialist competitive ant colony algorithm for truss structures. Applied Sciences, 12(33), 94–105.
Gandomi, A. H. (2014). Interior search algorithm (ISA): A novel approach for global optimization. ISA Transactions, 53(4), 1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018
Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010
Gupta, S., Abderazek, H., Yıldız, B. S., Yildiz, A. R., Mirjalili, S., & Sait, S. M. (2021). Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Systems with Applications, 183, 115351. https://doi.org/10.1016/j.eswa.2021.115351
Hasançebi, O., Çarbaş, S., Doğan, E., Erdal, F., & Saka, M. P. (2009). Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Computers & Structures, 87(5–6), 284–302. https://doi.org/10.1016/j.compstruc.2009.01.002
Holland, J. H. (1998). Adaptation in natural and artificial systems. University of Michigan Press.
Karaduman, A., Yıldız, B. S., & Yıldız, A. R. (2019). Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry. International Journal of Vehicle Design, 80(2–4), 330–345. https://doi.org/10.1504/IJVD.2019.109875
Kaveh, A., & Hosseini, P. (2014). A simplified dolphin echolocation optimization method for optimum design of trusses. International Journal of Optimization in Civil Engineering, 4(3), 381–397.
Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: A novel meta-heuristic method. Computers and Structures, 139, 18–27. https://doi.org/10.1016/j.compstruc.2014.04.005
Kaveh, A., Moghanni, R. M., & Javadi, S. M. (2019). Optimum design of large steel skeletal structures using chaotic firefly optimization algorithm based on the Gaussian map. Structural and Multidisciplinary Optimization, 60, 879–894. https://doi.org/10.1007/s00158-019-02263-1
Kaveh, A., & Talatahari, S. (2009a). A particle swarm ant colony optimization for truss structures with discrete variables. Journal of Constructional Steel Research, 65(8–9), 1558–1568. https://doi.org/10.1016/j.jcsr.2009.04.021
Kaveh, A., & Talatahari, S. (2009b). Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers & Structures, 87(5–6), 267–283. https://doi.org/10.1016/j.compstruc.2009.01.003
Kaveh, A., & Talatahari, S. (2010). Imperialist competitive algoritjm foer engineering design problems. Asian Journal of Civil Engineering, 11(6), 675–697.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 – International Conference on Neural Networks (pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
Kumar, Y., & Singh, P. K. (2018). Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Applied Intelligence, 48(9), 2681–2697. https://doi.org/10.1007/s10489-017-1096-8
Kumar, Y., & Singh, P. K. (2019). A chaotic teaching learning based optimization algorithm for clustering problems. Applied Intelligence, 49(3), 1036–1062. https://doi.org/10.1007/s10489-018-1301-4
Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194(36–38), 3902–3933. https://doi.org/10.1016/j.cma.2004.09.007
Li, L. J., Huang, Z. B., & Liu, F. (2009). A heuristic particle swarm optimization method for truss structures with discrete variables. Computers & Structures, 87(7–8), 435–443. https://doi.org/10.1016/j.compstruc.2009.01.004
Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: An optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, 24(7–8), 1867–1877. https://doi.org/10.1007/s00521-013-1433-8
Mahi, M., Baykan, Ö. K., & Kodaz, H. (2015). A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Applied Soft Computing, 30, 484–490. https://doi.org/10.1016/j.asoc.2015.01.068
Meng, Z., Li, G., Wang, X., Sait, S. M., & Yıldız, A. R. (2021). A comparative study of metaheuristic algorithms for reliability-based design optimization problems. Archives of Computational Methods in Engineering, 28, 1853–1869. https://doi.org/10.1007/s11831-020-09443-z
Öchsner, A. (2020). Partial differential equations of classical structural members. Springer. https://doi.org/10.1007/978-3-030-35311-7
Panagant, N., Pholdee, N., Bureerat, S., Kaen, K., Yıldız, A. R., & Sait, S. M. (2020). Seagull optimization algorithm for solving real-world design optimization problems. Materials Testing, 62(6), 640–644. https://doi.org/10.3139/120.111529
Panagant, N., Pholdee, N., Bureerat, S., Yildiz, A. R., & Mirjalili, S. (2021). A comparative study of recent multi-objective metaheuristics for solving constrained truss optimisation problems. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-021-09531-8
Price, K. V., & Storn, R. (1997). Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359. https://doi.org/10.1023/A:1008202821328
Rajeev, S., & Krishnamoorthy, C. S. (1992). Discrete optimization of structures using genetic algorithms. Journal of Structural Engineering, 118(5), 1233–1250. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:5(1233)
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015
Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2012). Mine blast algorithm for optimization of truss structures with discrete variables. Computers & Structures, 102–103, 49–63. https://doi.org/10.1016/j.compstruc.2012.03.013
Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026
Sarangkum, R., Wansasueb, K., Panagant, N., Pholdee, N., Bureerat, S., Yildiz, A. R., & Sait, S. M. (2019). Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation. International Journal of Vehicle Design, 80(2–4), 162–175. https://doi.org/10.1504/IJVD.2019.109864
Shahrouzi, M., & Kaveh, A. (2015). Dynamic fuzzy-membership optimization: an enhanced meta-heuristic search. Asian Journal of Civil Engineering, 16(2), 249–268.
Shahrouzi, M., Aghabagloua, M., & Rafiee, F. (2017). Observer-teacher-learner-based optimization: An enhanced meta-heuristic for structural sizing design. Structural Engineering and Mechanics, 62(5), 537–550.
Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713. https://doi.org/10.1109/TEVC.2008.919004
Strauss, A., Wan-Wendner, R., Vidovic, A., Zambon, I., Yu, Q., Frangopol, D. M., & Bergmeister, K. (2017). Gamma prediction models for long-term creep deformations of prestressed concrete bridges. Journal of Civil Engineering and Management, 23(6), 681–698. https://doi.org/10.3846/13923730.2017.1335652
Strauss, A., Krug, B., Slowik, O., & Novak, D. (2018). Combined shear and flexure performance of prestressing concrete T-shaped beams: Ex-periment and deterministic modeling. Structural Concrete, 19(1), 16–35. https://doi.org/10.1002/suco.201700079
Strauss, A., Mordini, A., & Bergmeister, K. (2006). Nonlinear finite element analysis of reinforced concrete corbels at both deterministic and probabilistic levels. Computers and Concrete, 3(2–3), 123–144. https://doi.org/10.1016/0045-7949(93)90199-N
Talaslioglu, T. (2019). Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology. Frontiers of Structural and Civil Engineering, 13(4), 863–889. https://doi.org/10.1007/s11709-019-0523-9
Talatahari, S., Kaveh, A., & Sheikholeslami, R. (2012a). Chaotic imperialist competitive algorithm for optimum design of truss structures. Structural and Multidisciplinary Optimization, 46(3), 355–367. https://doi.org/10.1007/s00158-011-0754-4
Talatahari, S., Nouri, M., & Tadbiri, F. (2012b). Optimization of skeletal structural using artificial bee colony algorithm. International Journal of Optimization in Civil Engineering, 2(4), 557–571.
Wang, G.-G., Gandomi, A. H., Zhao, X., & Chu, H. C. (2016). Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Computing, 20(1), 273–285. https://doi.org/10.1007/s00500-014-1502-7
Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. In J. R. González, D. A. Pelta, C. Cruz, G. Terrazas, & N. Krasnogor (Eds.), Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in computational intelligence: Vol. 284 (pp. 65–74). Springer. https://doi.org/10.1007/978-3-642-12538-6_6
Yıldız, A. R., & Erdaş, M. U. (2021). A new Hybrid Taguchi-salp swarm optimization algorithm for the robust design of real-world engineering problems. Materials Testing, 63(2), 157–162. https://doi.org/10.1515/mt-2020-0022
Yıldız, B. S., Yıldız, A. R., Pholdee, N., Bureerat, S., Sait, S. M., & Patel, V. (2020a). The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components. Materials Testing, 62(3), 261–264. https://doi.org/10.3139/120.111479
Yıldız, B. S., Yıldız, A. R., Albak, E. İ., Abderazek, H., Sait, S. M., & Bureerat, S. (2020b). Butterfly optimization algorithm for optimum shape design of automobile suspension components. Materials Testing, 62(4), 365–370. https://doi.org/10.3139/120.111492
Yıldız, A. R., Özkaya, H., Yıldız, M., Bureerat, S., Yıldız, B. S., & Sait, S. M. (2020c). The equilibrium optimization algorithm and the response surface-based metamodel for optimal structural design of vehicle components. Materials Testing, 62(5), 492–496. https://doi.org/10.3139/120.111509
Yıldız, A. B. S., Pholdee, N., Bureerat, S., Yıldız, A. R., & Sait, S. M. (2020d). Sine-cosine optimization algorithm for the conceptual design of automobile components. Materials Testing, 62(7), 744–748. https://doi.org/10.3139/120.111541
Yildiz, B. S., Pholdee, N., Bureerat, S., Yildiz, A. R., & Sait, S. M. (2021a). Robust design of a robot gripper mechanism using new hybrid grass-hopper optimization algorithm. Expert Systems, 38(3), e12666. https://doi.org/10.1111/exsy.12666
Yildiz, B. S., Pholdee, N., Bureerat, S., Yildiz, A. R., & Sait, S. M. (2021b). Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering prob-lems. Engineering with Computers. https://doi.org/10.1007/s00366-021-01368-w
Yıldız, B. S., Pholdee, N., Bureerat, S., Erdaş, M. U., Yıldız, A. R., & Sait, S. M. (2021c). Comparison of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry. Materials Testing, 63(4), 356–359. https://doi.org/10.1515/mt-2020-0053
Yıldız, B. S., Patel, V., Pholdee, N., Sait, S. M., Bureerat, S., & Yıldız, A. R. (2021d). Conceptual comparison of the ecogeography-based algo-rithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design. Materials Testing, 63(4), 336–340. https://doi.org/10.1515/mt-2020-0049
Zambon, I., Vidovic, A., Strauss, A., Matos, J., & Amado, J. (2017). Comparison of stochastic prediction models based on visual inspections of bridge decks. Journal of Civil Engineering and Management, 23(5), 553–561. https://doi.org/10.3846/13923730.2017.1323795