A Discrete Hybrid Teaching-Learning-Based Optimization algorithm for optimization of space trusses
الموضوعات :Siamak Talatahari 1 , Vahid Goodarzimehr 2
1 - Department of Civil Engineering, University of Tabriz, Tabriz, Iran
2 Engineering Faculty, Near East University, 99138 Nicosia, North Cyprus, 10 Mersin, Turkey
2 - Department of Civil Engineering, University of Tabriz, Tabriz, Iran
الکلمات المفتاحية: Discrete variables, Teaching-learning-based optimization, Harmony search, Size optimization, Truss structures, Structural optimization, Meta-heuristic algorithm,
ملخص المقالة :
In this study, to enhance the optimization process, especially in the structural engineering field two well-known algorithms are merged together in order to achieve an improved hybrid algorithm. These two algorithms are Teaching-Learning Based Optimization (TLBO) and Harmony Search (HS) which have been used by most researchers in varied fields of science. The hybridized algorithm is called A Discrete Hybrid Teaching-Learning Based Optimization (DHTLBO) that is applied to optimization of truss structures with discrete variables. This new method is consisted of two parts: in the first part the TLBO algorithm applied as conventional TLBO for local optimization, in the second stage the HS algorithm is applied to global optimization and exploring all the unknown places in the search space. The new hybrid algorithm is employed to minimize the total weight of structures. Therefore, the objective function consists of member’s weight, which is depends on the form of stress and deflection limits. To demonstrate the efficiency and robustness of this new algorithm several truss structures which are optimized by most researchers are presented and then their results are compared to other meta-heuristic algorithm and TLBO and HS standard algorithms.
[1] Rajeev, S. and Krishnamoorthy, CS., “Discrete optimization of structures using genetic algorithms”, J Struct Eng, 118(5):1233–50, 1992.
[2] Cao, G. “Optimized design of framed structures using a genetic algorithm. PhD thesis”, The University of Memphis, TN; 1996.
[3] Wu, SJ. And Chow, PT. “Steady-state genetic algorithms for discrete optimization of trusses”. Computers and Structures, 56(6),979-91,1995.
[4] Camp, CV. and Bichon, BJ. “Design of space trusses using ant colony optimization”. J Struct Eng, 130(5), 741–51, 2004.
[5] Lee, KS. And Geem, ZW. “ A new structural optimization method based on the harmony search algorithm”. Computers & Structures, 82(9–10), 781–98, 2004
[6] Lee, KS., Geem, ZW., Lee, SH. and Bae, KW. “The harmony search heuristic algorithm for discrete structural optimization”. Engineering Optimization, 37(7), 663-84, 2005.
[7] Camp, CV.” Design of space trusses using big bang–big crunch optimization”. J Struct Eng, 133(7), 999–1008, 2007.
[8] Kaveh, A. and Talatahari, S. “Size optimization of space trusses using big bang–big crunch algorithm”. Comput Struct, 87(17–18):1129–40, 2009.
[9] Rao, RV., Savsani, VJ., Vakharia, DP. “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”. Computer Aided Design, 43(3):303–15, 2011.
[10] Sonmez, M. “Artificial bee colony algorithm for optimization of truss structures”. Appl Soft Comput, 11(2), 2406–18, 2011.
[11] Kaveh, A. Talatahari, S. “A novel heuristic optimization method: charged system search”, Acta Mechanica, 213, 267–289, 2010.
[12] Kaveh, A. Talatahari, S. “A hybrid particle swarm and ant colony optimization for design of truss structures”. Asian Journal of Civil Engineering, 9(4), 329_48, 2008.
[13] Li, LJ. Huang, ZB. Liu, F. “A heuristic particle swarm optimization method for truss structures with discrete variables”. Computers and Structures, 87(78), 435:43, 2009.
[14] Kaveh, A. Talatahari, S.” A particle swarm ant colony optimization for truss structures with discrete variables”. Journal of Constructional Steel Research, 65,1558-1568, 2009.
[15] Camp, CV. Farshchin, M. Design of space trusses using modified teaching-learning based optimization. Computers & Structures, 62–63: 87–97, 2014.
[16] Lee, KS. Sang, Whan. Han. SW. and Geem, ZW. “Discrete size and Discrete continuous configuration optimization methods for truss structures using the harmony search algorithm”. Int. J. Optim. Civil Eng, 1, 107-126, 2011.
[17] Meshki, H. and Joghataie, A. “Structural optimization by spherical interpolation of objective function and constraints”. Scintia Iranica, 23(2), 548-557, 2016.
[18] Kaveh, A. and Ilchi, M. “Computer codes for colloding bodies optimization and its enhanced version”. Int. J. Optim. Civil Eng, 4(3),321-339, 2014.
[19] Kaveh, A. Ilchi, Ghazaan. M. “A new meta-heuristic algorithm: Vibrating particles system, Scientia Iranica”. International Journal of Science and Technology, 24: 551-566, 2017.
[20] Gandomi, A. H., Alavi, A. H. “Krill herd: A new bio-inspired optimization algorithm”. Communications in Nonlinear Science and Numerical Simulation, 17, 4831–4845, 2012.
[21] Mirjalili, S. and Lewis, A. “The whale optimization algorithm", Advence Engineering Software, 95: 51-67, 2016.
[22] Cheng, M-Y, Prayogo, D, Wu, Y-W, MarcellinusLukito M. “A Hybrid Harmony Search algorithm for discrete sizing optimization of truss structure”. Automation in Construction, 69: 21-33, 2016.
[23] Tuo, S, Yong, L, Deng, F, Li, Y, Lin Y, Lu, Q. “A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems”. Pone, 10-1371, 2017.
[24] Ouyang, H. B. Gao, L. Q. Kong, X. Y, Zou D. X, & Li S. “Teaching-learning based optimization with global crossover for global optimization problems”. Applied Mathematics and Computation, 265, 533–556, 2015.
[25] Kaveh, A. and Rahami, H. “Analysis, design and optimization of structures using force method and genetic algorithm”. Int. J. Numer. Methods Eng. 65, pp. 1570-1584, 2016.
[26] Sadollah, A., Eskandar, H., Bahreininejad, A. and Kim, J.H. “Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures”. Comput. Struct. 149, pp. 1-16, 2015.
[27] Kaveh, A. Talatahari, S. “Hybrid Algorithm of Harmony Search, Particle Swarm and Ant Colony for Structural Design Optimization”. In: Geem Z.W. (eds) Harmony Search Algorithms for Structural Design Optimization. Studies in Computational Intelligence, vol 239. Springer, Berlin, Heidelberg, 2009.
