Hybrid Teaching-Learning-Based Optimization and Harmony Search for Optimum Design of Space Trusses
الموضوعات :Siamak Talatahari 1 , Vahid Goodarzimehr 2 , Nasser Taghizadieh 3
1 - Department of Civil Engineering, University of Tabriz, Tabriz, Iran.
Engineering Faculty, Near East University, Turkey.
2 - Department of Civil Engineering, University of Tabriz, Tabriz, Iran
3 - Department of Civil Engineering, University of Tabriz, Tabriz, Iran
الکلمات المفتاحية: Harmony search, Teaching-learning-based optimization, Size optimization, Structural optimization, Continuous variables,
ملخص المقالة :
The Teaching-Learning-Based Optimization (TLBO) algorithm is a new meta-heuristic algorithm which recently received more attention in various fields of science. The TLBO algorithm divided into two phases: Teacher phase and student phase; In the first phase a teacher tries to teach the student to improve the class level, then in the second phase, students increase their level by interacting among themselves. But, due to the lack of additional parameter to calculate the distance between the teacher and the mean of students, it is easily trapped at the local optimum and make it unable to reach the best global for some difficult problems. Since the Harmony Search (HS) algorithm has a strong exploration and it can explore all unknown places in the search space, it is an appropriate complement to improve the optimization process. Thus, based on these algorithms, they are merged to improve TLBO disadvantages for solving the structural problems. The objective function of the problems is the total weight of whole members which depends on the strength and displacement limits. Indeed, to avoid violating the limits, the penalty function applied in the form of stress and displacement limits. To show the superiority of the new hybrid algorithm to previous well-known methods, several benchmark truss structures are presented. The results of the hybrid algorithm indicate that the new algorithm has shown good performance.
Adeli, H., Cheng, NT. (1994). Concurrent genetic algorithms for optimization of large structures. J Aerospace Engineering, 7:276–296
Behnamian, J. (2019). Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling. Journal of Optimization in Industrial Engineering, 12(2), 107-119. doi: 10.22094/joie.2018.671.1433
Camp, CV. (2007). Design of space trusses using big bang–big crunch optimization. J Structural Engineering, 133(7):999–1008
Camp, CV., Bichon, BJ. (2004). Design of space trusses using ant colony optimization. J Structural Engineering, 130(5):741–751
Camp, CV., Farshchin, M. (2014). Design of space trusses using modified teaching-learning based optimization. Computers & Structures. 62(63):87–97
Cao, G. (1996). Optimized design of framed structures using a genetic algorithm. PhD thesis. The University of Memphis TN.
Chen, D., Zou, F., Li, Z., Wang, J., Li, S. (2015). An improved teaching–learning-based optimization algorithm for solving global optimization problem. Information Sciences, 297(C):171–190
Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD thesis, Dip. Elettronicae Informazione, Politecnico di Milano, Italy
Erbatur, F., Hasancebi, O. (2000). Optimal design of planar and space structures with genetic algorithms. Computer and Structures, 75:209-224
Faezy razi, F., Shadloo, N. (2017). A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection. Journal of Optimization in Industrial Engineering, 10(22), 49-59. doi: 10.22094/joie.2017.276
Fattahi, P., Samouei, P. (2016). A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers. Journal of Optimization in Industrial Engineering, 9(20), 9-18. doi: 10.22094/joie.2016.246
Fallahian, S., Joghataie, A., Kazemi, M.T. (2010). Structural damage detection using time domain responses and Teaching-Learning-Based Optimization algorithm. Scientia Iranica, 25(6), 3088-3100
Farshi, B., Alinia, A. (2010). Sizing optimization of truss structures by method of centers and force formulation. International Journal of Solids and Structures, 47: 2508–2524
Hasancebi, O. (2008). Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures. Computers and Structures, 86:119-132
Kaveh, A. and Bijari, Sh. (2018). Simultaneous analysis, design and optimization of trusses via force method. Struct Eng Mech, Int J, 65(3), pp. 233-241
Kaveh, A., Ilchi, Ghazaan. M. (2017). A new meta-heuristic algorithm: Vibrating particles system. Scientia Iranica. International Journal of Science and Technology, 24: 551-566
Kaveh, A., Rahami, S. (2016). Analysis, design and optimization of structures using force method and genetic algorithm. Int. J. Numer. Methods Eng, 65, pp. 1570-1584
Kaveh, A., Talatahari, S. (2009). Size optimization of space trusses using big bang–big crunch algorithm. Computers & Structures, 87(17–18):1129–1140
Kaveh, A., Talatahari, S. (2009). Hybrid Algorithm of Harmony Search, Particle Swarm and Ant Colony for Structural Design Optimization. Computers & Structures, 3:642-03450
Kaveh, A., Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213: 267–289
Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks p, 1942–1948
Lamberti, L. (2008). An efficient simulated annealing algorithm for design optimization of truss structures. Computers & Structures, 86(19–20):1936–1953
Lee, KS., Geem, ZW. (2004). A new structural optimization method based on the harmony search algorithm.Computers & Structures, 82(9–10):781–798
Li, LJ., Huang, ZB., Liu, F., Wu, QH. (2007). A heuristic particle swarm optimizer for optimization of pin connected structures. Computers & Structures, 85(7–8):340–9
Omidinasab, F., Goodarzimehr, V. (2019). A Hybrid Particle Swarm Optimization and Genetic Algorithm for truss structures with discrete variables. Journal of Applied and Computational Mechanics, (), -. doi: 10.22055/jacm.2019.28992.1531
Perez, Ruben. E, Behdinan, Kamran. (2007). Particle Swarm Optimization in Structural Design. Itech Education and Publishing, Vienna, Austria.
Rao, RV., Savsani, VJ., Vakharia, DP. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer Aided Design, 43(3):303–315
Sonmez, M. (2011). Artificial bee colony algorithm for optimization of truss structures. Applied Soft Computing, 11(2):2406–2418
Talatahari, S., Goodarzimehr, V. (2019). A Discrete Hybrid Teaching-Learning-Based Optimization algorithm for optimization of space trusses. Journal of Structural Engineering and Geo-Techniques, 9(1), 0-0.
Tuo, S., Yong, L., Deng, F., Li, Y., Lin, Y., Lu, Q. (2017). A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems, Pone10-1371
