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Teaching-Learning-Based Optimization State-of-the-Art
Abstract
This paper circumscribes the state-of-the-art to the most recent potential immigrant algorithm in computational intelligence, familiarized as Teaching-Learning-Based Optimization (TLBO). The basic ideology of TLBO was the simulation of schoolroom cognitive process into algorithmic instructions with two essential functions teacher phase and learners phase. Broadly, TLBO is a population based algorithm, which uses its inhibited solution to extract universal solutions. This nature inspired meta-heuristic is the brainchild of R. V. Rao et al., and presently available in three releases Basic, Elitist and Improved versions. All these algorithms efficiency and effectiveness were experimentally proven in solving engineering optimization problems. The literature on TLBO and its versions, TLBO incorporated clustering approaches practiced by prospective researchers is archived in this survey.
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References
R. Storn, K. Price, “Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, Vol. 11, No. 4, 1997, pp. 341-359. doi:10.1023/A:1008202821328
R.V. Rao, V.J. Savsani, D.P. Vakharia, “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems”, Computer-Aided Design, 2011, 303–315, doi:10.1016/j.cad.2010.12.015
R.V. Rao, V.J. Savsani, D.P. Vakharia, “Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems”, Information Sciences, 183, 2012 1–15, doi:10.1016/j.ins.2011.08.006.
R. Venkata Rao, Vivek Patel, “Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems”, International Journal of Industrial Engineering Computations, 4, 2013, doi: 10.5267/j.ijiec.2012.09.001.
R. Venkata Rao, Vivek Patel, “An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems”, International Journal of Industrial Engineering Computations, 3, 2012, 535–560,doi: 10.5267/j.ijiec.2012.03.007.
R. Venkata Rao, Vivek Patel, “An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems”, Scientia Iranica D, 2013, 20 (3), 710–720, doi:10.1016/j.scient.2012.12.005
R. Venkata Rao, G.G. Waghmare, “A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions”, Journal of King Saud University Computer and Information Sciences, 2014, http://dx.doi.org/10.1016/j.jksuci.2013.12.004
Babak Amiri, “Application of Teaching-Learning-Based Optimization Algorithm on Cluster Analysis”, J. Basic. Appl. Sci. Res., 2(11), doi: 11795-11802, 2012
Suresh Chandra Satapathy, Anima Naik, K Parvathi, “A teaching learning based optimization based on orthogonal design for solving global optimization problems”, SpringerPlus, 2013, 2:130
Anima Naik, Suresh Chandra Satapathy, K.Parvathi, “Improvement of initial cluster center of c-means using Teaching learning based optimization”, Procedia Technology, 6, pp. 428–435, 2012, doi: 10.1016/j.protcy.2012.10.051
M. Ramakrishna Murty et al., “Automatic Clustering Using Teaching Learning Based Optimization”, Applied Mathematics, 2014, 5, pp. 1202-1211, http://dx.doi.org/10.4236/am.2014.58111.
Ramachandra Rao Kurada, K. Karteeka Pavan and Allam Appa Rao, “Automatic Teaching–Learning-Based Optimization: A Novel Clustering Method for Gene Functional Enrichments”, Computational Intelligence Techniques for Comparative Genomics, Forensic and Medical Bioinformatics, doi: 10.1007/978-981-287-338-5_2, 2015, Springer, Vol. XVIII, pp. 17-35, ISBN.978-981-287-337-8.
Kanadam Karteeka Pavan et al., “An automatic medical image segementation using Teaching–Learning-Based Optimization”, Proceedings of International Conference on Advances in Computer Science, ACEEE, 2013, doi. 02AETACS2013.4.99
Deb K., “An efficient constraint handling method for genetic algorithm”, Computer Methods in Applied Mechanics and Engineering, 2000, 186, pp. 311–338.
Back T. “Evolutionary algorithms in theory and practice”, Oxford University Press, 1996.
M. Dorigo, “Optimization, Learning and Natural Algorithms”, Ph.D. Thesis, Politecnico diMilano, Italy, 1992.
J.C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press,NewYork, 1981.
Murthy, C. A., Chowdhury, N., “In search of optimal clusters using genetic algorithms”, Pattern Recognition Letters, vol.17, pp 825–832. 1996.
Al-Sultan, K. S., “A tabu search approach to the clustering problem” Pattern Recognition,Vol.28, No.9, pp 1443–1451, 1995.
Selim, S. Z., Al-Sultan, K. “A simulated annealing algorithm for the clustering problem”, Pattern Recognition, Vol.24, No.10, pp 1003–1008, 1991.
Shelokar, P. S., Jayaraman, V. K., Kulkarni, B. D, “An ant colony approach for clustering”, Analytica Chimica Acta, Vol. 509, pp 187–195, 2004.
Kao, Y.-T., Zahara, E., Kao, I.-W., “A hybridized approach to data clustering”, ExpertSystems with Applications, Vol. 34, No.3, pp 1754–1762, 2008.
Bache, K., Lichman, M., “UCI Machine Learning Repository”, [http://archive.ics.uci.edu/ml], Irvine, CA: University of California, School of Information and Computer Science, 2013
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