A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Yugal Kumar, Neeraj Dahiya, Sanjay Malik, Savita Khatri


This paper proposes a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems to improve the shortcoming of TLBO. The proposed algorithm uses the genetic crossover and mutation strategies for improving the search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find good learner. The results are taken on six well known benchmark test functions. From results, it is observed that the proposed algorithm provides more optimized results in comparison to same class of algorithms.

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Stutzle, T. G. ‘‘Local Search Algorithms for Combinatorial Problems: Analysis,Improvements, and New Applications.’’ PhD Thesis, Technical University of Darmstadt, Darmstadt, Germany, 1998.

S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing., Science. 220, (1983), 671–680.

J. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, University of Michigan Press, Ann Arbor, 1975.

R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, MHS’95. Proc. Sixth Int. Symp. Micro Mach. Hum. Sci. (1995) 39–43.

M. Dorigo, M. Birattari, T. Stützle, Ant colony optimization artificial ants as a computational intelligence technique, IEEE Comput. Intell. Mag. 1 (2006) 28–39.

K.S. Lee, Z.W. Geem, A new structural optimization method based on the harmony search algorithm, Comput. Struct. 82 (2004) 781–798. doi:10.1016/j.compstruc.2004.01.002.

D. Karaboga, B. Basturk, Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization, Lnai 4529. (2007) 789–798.

X.S. Yang, Firefly algorithms for multimodal optimization, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 5792 LNCS (2009) 169–178.

A. Husseinzadeh Kashan, An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA), Comput. Des. 43 (2011) 1769–1792.

H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi, Water cycle algorithm - A novel metaheuristicoptimization method for solving constrained engineering optimization problems, Comput. Struct. 110-111 (2012) 151–166.

Kaveh A, Talatahari S (2010) A novel heuristic optimization method:charged system search. Acta Mechanica 213(3–4):267–289

Kumar Y, Sahoo G (2014) A charged system search approach for data clustering. Progress Artif Intell 2(2–3):53–166.

Kaveh A, Share AMAM, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mechanica 224(1):85–107

Kumar, Y. and Sahoo, G., 2015. Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy. Soft Computing, 19(12), pp.3621-3645.

Kumar Y, Gupta S. and Sahoo G, A Clustering Approach Based on Charged Particles, International Journal of Software Engineering and Its Applications, Vol. 10, No. 3 (2016), pp. 9-28.

Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des 43(3):303–315

Sahoo AJ, Kumar Y (2014) Advances in signal processing and intelligent recognition systems, Modified teacher learning based optimization method for data clustering Springer, Berlin, pp. 429-437.

A. Sadollah, A. Bahreininejad, H. Eskandar, M. Hamdi, Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems, Appl. Soft Comput. J. 13 (2013), 2592–2612. doi:10.1016/j.asoc.2012.11.026.

Rao, R.V., Savsani, V.J. and Balic, J., 2012. Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, 44(12), pp.1447-1462.

R.V. Rao, V.J. Savsani, D.P. Vakharia, Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems,Inf. Sci. 183 (1) (2012) 1–15.

Z.L. Yang, K. Li, Q. Niu, et al., A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads, J. Mod. Power Syst. Clean Energy 2 (4) (2014) 298–307.

C.H. Chen, Group leader dominated teaching–learning based optimization, in:International Conference on Parallel and Distributed Computing, Applications and Technologies, 2013, pp. 304–308.

Z.L. Yang, K. Li, A.F. Foley, et al., A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles, in: IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 2014, pp. 2685–2691.

Sahoo, A.J. and Kumar, Y., 2014. Modified teacher learning based optimization method for data clustering. In Advances in Signal Processing and Intelligent Recognition Systems (pp. 429-437). Springer International Publishing.

Rao, R.V. and Patel, V., 2013. An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), pp.710-720.

Satapathy, S.C. and Naik, A., 2014. Modified Teaching–Learning-Based Optimization algorithm for global numerical optimization—A comparative study. Swarm and Evolutionary Computation, 16, pp.28-37.

Huang, J., Gao, L. and Li, X., 2015. An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Applied Soft Computing, 36, pp.349-356.

Zou, F., Wang, L., Hei, X. and Chen, D., 2015. Teaching–learning-based optimization with learning experience of other learners and its application. Applied Soft Computing, 37, pp.725-736.

Ouyang, H.B., Gao, L.Q., Kong, X.Y., Zou, D.X. and Li, S., 2015. Teaching-learning based optimization with global crossover for global optimization problems. Applied Mathematics and Computation, 265, pp.533-556.

Ghasemi, M., Taghizadeh, M., Ghavidel, S., Aghaei, J. and Abbasian, A., 2015. Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 39, pp.100-108.

Zou, F., Wang, L., Hei, X., Chen, D. and Yang, D., 2014. Teaching–learning-based optimization with dynamic group strategy for global optimization. Information Sciences, 273, pp.112-131.

Lim, W.H. and Isa, N.A.M., 2014. An adaptive two-layer particle swarm optimization with elitist learning strategy. Information Sciences, 273, pp.49-72.

Sahoo, G., 2015. A two-step artificial bee colony algorithm for clustering. Neural Computing and Applications, pp.1-15.

Ghasemi, M., Ghavidel, S., Rahmani, S., Roosta, A. and Falah, H., 2014. A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions. Engineering Applications of Artificial Intelligence, 29, pp.54-69.

Ghasemi, M., Ghanbarian, M.M., Ghavidel, S., Rahmani, S. and Moghaddam, E.M., 2014. Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: a comparative study. Information Sciences, 278, pp.231-249.

DOI: https://doi.org/10.31449/inf.v43i1.1636

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