How to cite this paper
Satapathy, S & Naik, A. (2013). Improved teaching learning based optimization for global function optimization.Decision Science Letters , 2(1), 23-34.
Refrences
Alatas, A. (2010). Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications, 37, 5682–5687.
Das, S., Abraham, A., & Konar A. (2008). Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE transactions on systems, man, and cybernetics—part a: systems and humans, 38(1).
Das, S., Abraham, A., Chakraborty, U.K., & Konar, A. (2009). Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computations, 13, 526–553.
Gao, W., & Liu, S. (2011). Improved artificial bee colony algorithm for global optimization. Information Processing Letters, 111, 871–882.
Krishnanand, K.R., Panigrahi, B. K., Rout, P.K., & Mohapatra, A. (2011). Application of multi-objective teaching learning based algorithm to an economic load dispatch problem with incommensurable objectives. Lecture Notes in Computer Science, SEMCCO 2011, Part I, 7076, 697-705.
Kang, F., Li, J. J., & Ma, Z.Y. (2011). Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences, 12, 3508–3531.
Naik, A., K., Parvathi, Satapathy, S. C., Nayak, R., & Panda, B. S. (2012). QoS multicast routing using teaching learning based optimization, ICAdC 2012. Advances in Intelligent and Soft Computing Series, Springer.
Naik, A., Satapathy, S. C. Rough set and Teaching learning based optimization technique for Optimal Features Selection. Central European Journal of Computer Science, to appear.
Naik, A., Satapathy, S. C., Parvathi, K. (2012). Improvement of initial cluster center of c- means using teaching learning based optimization. Procedia Technology, to appear.
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, 43,303–315
Rao, R.V., Savsani, V.J., & Vakharia, D.P. (2012). Teaching-learning-based optimization: a novel optimization method for continuous non-linear large scale problems. Information Sciences, 183, 1–15.
Rao, R.V., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving
complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
Rao, R.V., & Patel, V.K. (2012). Multi-objective optimization of combined Brayton and inverse
Brayton cycles using advanced optimization algorithms. Engineering Optimization, 44(8), 965-983.
Rao, R.V., & Savsani, V.J. (2012). Mechanical design optimization using advanced optimization techniques. Springer-Verlag London, UK.
Rao, R. V., & Kalyankar, V. D. (2012). Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes, 27(9), 978-985.
Ratnaweera, A., Halgamuge, S., Watson, H. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computers, 8, 240–255
Satapathy, S. C., & Naik, A. (2011). Data clustering using teaching learning based Optimization.
SEMCCO 2011. Part II, Lecture Notes in Computer Science, 7077, 148–156.
Satapathy, S.C., Naik, A., K., Parvathi (2012). High dimensional real parameter optimization with teaching learning based optimization. International Journal of Industrial Engineering Computations, 3(5), 807-816.
Satapathy, S.C., Naik., A., & Parvathi, K. (2012). 0-1 integer programming for generation maintenance scheduling in power systems based on teaching learning based optimization (TLBO). IC3 2012. Communications in Computer and Information Science (CCIS) series, to appear.
To?an, V. (2012). Design of planar steel frames using Teaching–Learning Based Optimization. Engineering Structures. 34, 225–232.
Zhan, Z.H., Zhang, J., Li, Y., Chung, S.H. (2009). Adaptive particle swarm optimization. IEEE Transactions in Systems Man Cybernetic B Cybernetic, 39(6), 1362–1381.
Zhu, G.P., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function. Applied Soft Computing, 10(2), 445-456.
Das, S., Abraham, A., & Konar A. (2008). Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE transactions on systems, man, and cybernetics—part a: systems and humans, 38(1).
Das, S., Abraham, A., Chakraborty, U.K., & Konar, A. (2009). Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computations, 13, 526–553.
Gao, W., & Liu, S. (2011). Improved artificial bee colony algorithm for global optimization. Information Processing Letters, 111, 871–882.
Krishnanand, K.R., Panigrahi, B. K., Rout, P.K., & Mohapatra, A. (2011). Application of multi-objective teaching learning based algorithm to an economic load dispatch problem with incommensurable objectives. Lecture Notes in Computer Science, SEMCCO 2011, Part I, 7076, 697-705.
Kang, F., Li, J. J., & Ma, Z.Y. (2011). Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences, 12, 3508–3531.
Naik, A., K., Parvathi, Satapathy, S. C., Nayak, R., & Panda, B. S. (2012). QoS multicast routing using teaching learning based optimization, ICAdC 2012. Advances in Intelligent and Soft Computing Series, Springer.
Naik, A., Satapathy, S. C. Rough set and Teaching learning based optimization technique for Optimal Features Selection. Central European Journal of Computer Science, to appear.
Naik, A., Satapathy, S. C., Parvathi, K. (2012). Improvement of initial cluster center of c- means using teaching learning based optimization. Procedia Technology, to appear.
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, 43,303–315
Rao, R.V., Savsani, V.J., & Vakharia, D.P. (2012). Teaching-learning-based optimization: a novel optimization method for continuous non-linear large scale problems. Information Sciences, 183, 1–15.
Rao, R.V., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving
complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
Rao, R.V., & Patel, V.K. (2012). Multi-objective optimization of combined Brayton and inverse
Brayton cycles using advanced optimization algorithms. Engineering Optimization, 44(8), 965-983.
Rao, R.V., & Savsani, V.J. (2012). Mechanical design optimization using advanced optimization techniques. Springer-Verlag London, UK.
Rao, R. V., & Kalyankar, V. D. (2012). Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes, 27(9), 978-985.
Ratnaweera, A., Halgamuge, S., Watson, H. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computers, 8, 240–255
Satapathy, S. C., & Naik, A. (2011). Data clustering using teaching learning based Optimization.
SEMCCO 2011. Part II, Lecture Notes in Computer Science, 7077, 148–156.
Satapathy, S.C., Naik, A., K., Parvathi (2012). High dimensional real parameter optimization with teaching learning based optimization. International Journal of Industrial Engineering Computations, 3(5), 807-816.
Satapathy, S.C., Naik., A., & Parvathi, K. (2012). 0-1 integer programming for generation maintenance scheduling in power systems based on teaching learning based optimization (TLBO). IC3 2012. Communications in Computer and Information Science (CCIS) series, to appear.
To?an, V. (2012). Design of planar steel frames using Teaching–Learning Based Optimization. Engineering Structures. 34, 225–232.
Zhan, Z.H., Zhang, J., Li, Y., Chung, S.H. (2009). Adaptive particle swarm optimization. IEEE Transactions in Systems Man Cybernetic B Cybernetic, 39(6), 1362–1381.
Zhu, G.P., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function. Applied Soft Computing, 10(2), 445-456.