How to cite this paper
Rao, R & Patel, V. (2013). Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems.International Journal of Industrial Engineering Computations , 4(1), 29-50.
Refrences
Ahrari, A. & Atai A. A. (2010). Grenade explosion method - A novel tool for optimization of multimodal functions. Applied Soft Computing, 10, 1132-1140.
Azizipanah-Abarghooee, R., Niknam, T., Roosta, A., Malekpour, A.R. & Zare, M. (2012). Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method, Energy, 37, 322-335.
Basturk, B & Karaboga, D. (2006). An artificial bee colony (ABC) algorithm for numeric function optimization, in: IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA.
?repin?ek, M., Liu, S-H & Mernik, L. (2012). A note on teaching-learning-based optimization algorithm, Information Sciences, 212, 79-93.
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.
Hedar, A. & Fukushima, M. (2006). Evolution strategies learned with automatic termination criteria. Proceedings of SCIS-ISIS 2006, Tokyo, Japan.
Holland, J. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
Hosseinpour, H., Niknam, T. & Taheri, S.I. (2011). A modified TLBO algorithm for placement of AVRs considering DGs, 26th International Power System Conference, 31st October – 2nd November 2011, Tehran, Iran.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Computer Engineering Department. Erciyes University, Turkey.
Karaboga, D. & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1) 108-132.
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. (2008). On the performance of artificial bee colony (ABC) algorithm.
Applied Soft Computing, 8 (1), 687–697.
Kashan, A.H. (2011). An efficient algorithm for constrained global optimization and application to
mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design,
43, 1769-1792.
Kennedy, J. & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International
Conference on Neural Networks, IEEE Press, Piscataway, 1942-1948.
Krishnanand, K.R., Panigrahi, B.K., Rout, P.K. & Mohapatra, A. (2011). Application of multiobjective teaching-learning-based algorithm to an economic load dispatch problem with incommensurable objectives. Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science 7076, 697-705, Springer-Verlag, Berlin.
Milano, M., Koumoutsakos, P. & Schmidhuber, J. (2004). Self-organizing nets for optimization. IEEE Transactions on Neural Networks, 2004, 15(3), 758-765.
Nayak, N., Routray, S.K. & Rout, P.K. (2011). A robust control strategies to improve transient stability in VSC- HVDC based interconnected power systems. Proc. of IEEE Conference on Energy, Automation, and Signal (ICEAS), PAS-102, 1-8.
Niknam, T., Fard, A.K. & Baziar, A. (2012a). Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants, Energy, 42, 563-573.
Niknam, T., Golestaneh, F., & Sadeghi, M.S. (2012b). ?-multiobjective teaching–learning-based optimization for dynamic economic emission dispatch. IEEE Systems Journal, 6, 341-352.
Niknam, T., Azizipanah-Abarghooee, R. & Narimani, M.R. (2012c). A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Engineering Applications of Artificial Intelligence,
http://dx.doi.org/10.1016/j.engappai.2012.07.004.
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. & Kalyankar, V.D. (2012a). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, http://dx.doi.org/10.1016/j.engappai.2012.06.007.
Rao, R.V. & Kalyankar, V.D. (2012b). Multi-objective multi-parameter optimization of the industrial LBW process using a new optimization algorithm. Journal of Engineering Manufacture, DOI: 10.1177/0954405411435865
Rao, R.V. & Kalyankar, V.D. (2012c). Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes, DOI: 10.1080/10426914.2011.602792
Rao, R.V. & Patel, V. (2012a). 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. (2012b). Multi-objective optimization of combined Brayton and inverse Brayton cycle using advanced optimization algorithms, Engineering Optimization, doi: 10.1080/0305215X.2011.624183.
Rao, R.V. & Patel, V. (2012c). Multi-objective optimization of heat exchangers using a modified teaching-learning-based-optimization algorithm, Applied Mathematical Modeling, doi:10.1016/j.apm.2012.03.043.
Rao, R.V. & Patel, V. (2012d). Multi-objective optimization of two stage thermoelectric cooler using a modified teaching-learning-based-optimization algorithm. Engineering Applications of Artificial
Intelligence, doi:10.1016/j.engappai.2012.02.016
Rao, R.V. & Savsani, V.J. (2012). Mechanical design optimization using advanced optimization techniques. Springer-Verlag, London.
Rao, R.V., Savsani, V.J & Balic, J. (2012b). Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, http://dx.doi.org/10.1080/0305215X.2011.652103
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., Savsani, V.J. & Vakharia, D.P. (2012a). Teaching-learning-based optimization: A novel optimization method for continuous non-linear large scale problems. Information Sciences, 183 (1),1-15.
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.
Satapathy, S.C. & Naik, A. (2011). Data clustering based on teaching-learning-based optimization.
Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science 7077, 148-156,Springer-Verlag, Berlin.
Satapathy, S.C., Naik, A. & Parvathi, K. (2012). High dimensional real parameter optimization with teaching learning based optimization. International Journal of Industrial Engineering Computations, doi: 10.5267/j.ijiec.2012.06.001.
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.
To?an, V. (2012). Design of planar steel frames using teaching–learning based optimization,
Engineering Structures, 34, 225–232.
Yao, X & Liu, Y. (1997). Fast evolution strategies. Control and Cybernetics, 26(3), 467- 496.
Azizipanah-Abarghooee, R., Niknam, T., Roosta, A., Malekpour, A.R. & Zare, M. (2012). Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method, Energy, 37, 322-335.
Basturk, B & Karaboga, D. (2006). An artificial bee colony (ABC) algorithm for numeric function optimization, in: IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA.
?repin?ek, M., Liu, S-H & Mernik, L. (2012). A note on teaching-learning-based optimization algorithm, Information Sciences, 212, 79-93.
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.
Hedar, A. & Fukushima, M. (2006). Evolution strategies learned with automatic termination criteria. Proceedings of SCIS-ISIS 2006, Tokyo, Japan.
Holland, J. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
Hosseinpour, H., Niknam, T. & Taheri, S.I. (2011). A modified TLBO algorithm for placement of AVRs considering DGs, 26th International Power System Conference, 31st October – 2nd November 2011, Tehran, Iran.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Computer Engineering Department. Erciyes University, Turkey.
Karaboga, D. & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1) 108-132.
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. (2008). On the performance of artificial bee colony (ABC) algorithm.
Applied Soft Computing, 8 (1), 687–697.
Kashan, A.H. (2011). An efficient algorithm for constrained global optimization and application to
mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design,
43, 1769-1792.
Kennedy, J. & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International
Conference on Neural Networks, IEEE Press, Piscataway, 1942-1948.
Krishnanand, K.R., Panigrahi, B.K., Rout, P.K. & Mohapatra, A. (2011). Application of multiobjective teaching-learning-based algorithm to an economic load dispatch problem with incommensurable objectives. Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science 7076, 697-705, Springer-Verlag, Berlin.
Milano, M., Koumoutsakos, P. & Schmidhuber, J. (2004). Self-organizing nets for optimization. IEEE Transactions on Neural Networks, 2004, 15(3), 758-765.
Nayak, N., Routray, S.K. & Rout, P.K. (2011). A robust control strategies to improve transient stability in VSC- HVDC based interconnected power systems. Proc. of IEEE Conference on Energy, Automation, and Signal (ICEAS), PAS-102, 1-8.
Niknam, T., Fard, A.K. & Baziar, A. (2012a). Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants, Energy, 42, 563-573.
Niknam, T., Golestaneh, F., & Sadeghi, M.S. (2012b). ?-multiobjective teaching–learning-based optimization for dynamic economic emission dispatch. IEEE Systems Journal, 6, 341-352.
Niknam, T., Azizipanah-Abarghooee, R. & Narimani, M.R. (2012c). A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Engineering Applications of Artificial Intelligence,
http://dx.doi.org/10.1016/j.engappai.2012.07.004.
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. & Kalyankar, V.D. (2012a). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, http://dx.doi.org/10.1016/j.engappai.2012.06.007.
Rao, R.V. & Kalyankar, V.D. (2012b). Multi-objective multi-parameter optimization of the industrial LBW process using a new optimization algorithm. Journal of Engineering Manufacture, DOI: 10.1177/0954405411435865
Rao, R.V. & Kalyankar, V.D. (2012c). Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes, DOI: 10.1080/10426914.2011.602792
Rao, R.V. & Patel, V. (2012a). 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. (2012b). Multi-objective optimization of combined Brayton and inverse Brayton cycle using advanced optimization algorithms, Engineering Optimization, doi: 10.1080/0305215X.2011.624183.
Rao, R.V. & Patel, V. (2012c). Multi-objective optimization of heat exchangers using a modified teaching-learning-based-optimization algorithm, Applied Mathematical Modeling, doi:10.1016/j.apm.2012.03.043.
Rao, R.V. & Patel, V. (2012d). Multi-objective optimization of two stage thermoelectric cooler using a modified teaching-learning-based-optimization algorithm. Engineering Applications of Artificial
Intelligence, doi:10.1016/j.engappai.2012.02.016
Rao, R.V. & Savsani, V.J. (2012). Mechanical design optimization using advanced optimization techniques. Springer-Verlag, London.
Rao, R.V., Savsani, V.J & Balic, J. (2012b). Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, http://dx.doi.org/10.1080/0305215X.2011.652103
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., Savsani, V.J. & Vakharia, D.P. (2012a). Teaching-learning-based optimization: A novel optimization method for continuous non-linear large scale problems. Information Sciences, 183 (1),1-15.
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.
Satapathy, S.C. & Naik, A. (2011). Data clustering based on teaching-learning-based optimization.
Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science 7077, 148-156,Springer-Verlag, Berlin.
Satapathy, S.C., Naik, A. & Parvathi, K. (2012). High dimensional real parameter optimization with teaching learning based optimization. International Journal of Industrial Engineering Computations, doi: 10.5267/j.ijiec.2012.06.001.
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.
To?an, V. (2012). Design of planar steel frames using teaching–learning based optimization,
Engineering Structures, 34, 225–232.
Yao, X & Liu, Y. (1997). Fast evolution strategies. Control and Cybernetics, 26(3), 467- 496.