How to cite this paper
Rao, R & 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.
Refrences
Ahrari, A. & Atai A. A. (2010). Grenade explosion method - A novel tool for optimization of multimodal functions. Applied Soft Computing, 10, 1132-1140.
Basturk, B & Karaboga, D. (2006). An artificial bee colony (ABC) algorithm for numeric function optimization, in: IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA.
Deb, K. (2000). An efficient constraint handling method for genetic algorithm. Computer Methods in Applied Mechanics and Engineering, 186, 311–338.
Dorigo, M., Maniezzo V. & Colorni A. (1991). Positive feedback as a search strategy, Technical Report 91-016. Politecnico di Milano, Italy.
Eusuff, M. & Lansey, E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management, 29, 210-225.
Farmer, J. D., Packard, N. & Perelson, A. (1986).The immune system, adaptation and machine learning, Physica D, 22,187-204.
Fogel, L. J, Owens, A. J. & Walsh, M.J. (1966). Artificial intelligence through simulated evolution. John Wiley, New York.
Geem, Z. W., Kim, J.H. & Loganathan G.V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76, 60-70.
Holland, J. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
Karaboga, D. & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8 (1), 687–697.
Karaboga, D. & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39 (3), 459–471.
Karaboga, D. & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 4529, 789-798.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Computer Engineering Department. Erciyes University, Turkey.
Kennedy, J. & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, IEEE Press, Piscataway, 1942-1948.
Liang, J.J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello, C.A.C, Deb, K. (2006). Problem definitions and evaluation criteria for the CEC special session on constrained real-parameter optimization, Technical Report, Nanyang Technological University. Singapore, http://www.ntu.edu.sg/home/EPNSugan.
Mallipeddi, R. & Suganthan, P.N. (2010). Ensemble of constraint handling techniques. IEEE Transactions on Evolutionary Computation, 14 (4), 561-579.
Passino, K.M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22, 52–67.
Price K., Storn, R, & Lampinen, A. (2005). Differential evolution - a practical approach to global optimization, Springer Natural Computing Series.
Rao, R.V. & Savsani, V.J. (2012). Mechanical design optimization using advanced optimization techniques. Springer-Verlag, London.
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), 1-15.
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 (3), 303-315.
Rao, R.V. & Patel, V.K. (2012). Multi-objective optimization of combined Brayton abd reverse Brayton cycles using advanced optimization algorithms. Engineering Optimization, DOI: 10.1080/0305215X.2011.624183.
Rashedi, E., Nezamabadi-pour, H. & Saryazdi, S. (2009). GSA: A gravitational search algorithm, Information Sciences, 179, 2232-2248.
Runarsson, T.P. & Yao X. (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4 (3), 284-294.
Simon, D. (2008) Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12, 702–713.
Storn, R. & Price, K. (1997). Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341-359.
Basturk, B & Karaboga, D. (2006). An artificial bee colony (ABC) algorithm for numeric function optimization, in: IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA.
Deb, K. (2000). An efficient constraint handling method for genetic algorithm. Computer Methods in Applied Mechanics and Engineering, 186, 311–338.
Dorigo, M., Maniezzo V. & Colorni A. (1991). Positive feedback as a search strategy, Technical Report 91-016. Politecnico di Milano, Italy.
Eusuff, M. & Lansey, E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management, 29, 210-225.
Farmer, J. D., Packard, N. & Perelson, A. (1986).The immune system, adaptation and machine learning, Physica D, 22,187-204.
Fogel, L. J, Owens, A. J. & Walsh, M.J. (1966). Artificial intelligence through simulated evolution. John Wiley, New York.
Geem, Z. W., Kim, J.H. & Loganathan G.V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76, 60-70.
Holland, J. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
Karaboga, D. & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8 (1), 687–697.
Karaboga, D. & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39 (3), 459–471.
Karaboga, D. & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 4529, 789-798.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Computer Engineering Department. Erciyes University, Turkey.
Kennedy, J. & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, IEEE Press, Piscataway, 1942-1948.
Liang, J.J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello, C.A.C, Deb, K. (2006). Problem definitions and evaluation criteria for the CEC special session on constrained real-parameter optimization, Technical Report, Nanyang Technological University. Singapore, http://www.ntu.edu.sg/home/EPNSugan.
Mallipeddi, R. & Suganthan, P.N. (2010). Ensemble of constraint handling techniques. IEEE Transactions on Evolutionary Computation, 14 (4), 561-579.
Passino, K.M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 22, 52–67.
Price K., Storn, R, & Lampinen, A. (2005). Differential evolution - a practical approach to global optimization, Springer Natural Computing Series.
Rao, R.V. & Savsani, V.J. (2012). Mechanical design optimization using advanced optimization techniques. Springer-Verlag, London.
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), 1-15.
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 (3), 303-315.
Rao, R.V. & Patel, V.K. (2012). Multi-objective optimization of combined Brayton abd reverse Brayton cycles using advanced optimization algorithms. Engineering Optimization, DOI: 10.1080/0305215X.2011.624183.
Rashedi, E., Nezamabadi-pour, H. & Saryazdi, S. (2009). GSA: A gravitational search algorithm, Information Sciences, 179, 2232-2248.
Runarsson, T.P. & Yao X. (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 4 (3), 284-294.
Simon, D. (2008) Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12, 702–713.
Storn, R. & Price, K. (1997). Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341-359.