How to cite this paper
Rao, R & Patel, V. (2014). A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems.International Journal of Industrial Engineering Computations , 5(1), 1-22.
Refrences
Agrawal, S., Dashora, Y., Tiwari, M.K. & Son Y.J. (2008) “Interactive particle swarm: a Pareto-adaptive meta heuristic to multi-objective optimization”, IEEE T. Syst. Man Cy. A, 38(2), 258–277.
Akbari, R. & Ziarati , K. (2012). Multi-objective bee swarm optimization. Int. J. Innov. Comput. I., 8 (1-B), 715-726.
Chen, C.M., Chen, Y. & Zhang, Q. (2009). Enhancing MOEA/D with guided mutation & priority update for multi-objective optimization. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 209–216.
Coello Coello, C.A., Lamont, G.B. & Van Veldhuizen, D.A., Evolutionary Algorithms for Solving Multi-Objective Problems. Springer-Verlag (2007).
Coello Coello, C.A., Pulido ,G.T. & Lechuga, M.S. (2004). H & ling multiple objectives with particle swarm optimization. IEEE T. Evolut. Comput., 8(3), 256-279.
Deb, K., Mohan, M. & Mishra, S. (2005) “Evaluating the epsilon-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions”, Evol. Comput.,13(4), 501–525.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast & elitist multi-objective genetic algorithm: NSGA-II. IEEE T. Evolut. Comput., 6(2), 182-197.
Huang, V.L., Zhao, S.Z., Mallipeddi, R. & Suganthan, P.N. (2009). Multi-objective optimization using self-adaptive differential evolution algorithm. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 190–194.
Liu, H. & Li, X. (2009). The multi-objective evolutionary algorithm based on determined weight & sub-regional search. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1928–1934.
Liu, M., Zou, X., Chen, Y. & Wu, Z. (2009). Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances” In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2913-2918.
Gao, S., Zeng, S., Xiao, B., Zhang, L., Shi, Y., Tian, X., Yang, Y., Long, H., Yang, X., Yu, D. & Yan, Z. (2009). An orthogonal multi-objective evolutionary algorithm with lower-dimensional crossover. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1959–1964.
Hedayatzadeh, R,, Hasanizadeh, B., Akbari, R. & Ziarati, K. (2010). A multi-objective artificial bee colony for optimizing multi-objective problems. In: 3rd International Conference on Advanced Computer Theory & Engineering (ICACTE ), 5, 271–281.
Karaboga, D., Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm, Appl. Math. Comput. 214, 108–132.
Kukkonen, S. & Lampinen, J. (2009). Performance assessment of generalized differential evolution with a given set of constrained multi-objective test problems. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1943–1950.
Leong, W.F. & Yen, G.G. (2008). PSO-based multi-objective optimization with dynamic population size & adaptive local archives. IEEE T. Syst. Man Cy. B, 38(5), 1270–1293.
Mostaghim, S. & Teich, J. (2004). Covering Pareto-optimal fronts by sub swarms in multi-objective particle swarm optimization. In: 2004 IEEE Congress on Evolutionary Computation, 19-23 June, Portl & , USA, 1404–1411.
Qu, B.Y. & Suganthan, P.N. (2011). Constrained multi-objective optimization algorithm with ensemble of constraint h & ling methods. Eng. Optimiz., 43(4), 403-434.
Qu, B.Y. & Suganthan, P.N. (2009). Multi-objective evolutionary programming without non-domination sorting is up to twenty times faster. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2934–2939.
Rao, R.V., Savsani, V.J. & Vakharia, D.P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput. Aided Design, 43(3), 303-315.
Rao, R.V., Savsani, V.J. & Vakharia, D.P. (2012). Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems. Inform. Sciences, 183(1), 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. (2013a). Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. International Journal of Industrial Engineering Computations, doi: 10.5267/j.ijiec.2012.09.001.
Rao, R.V. & Patel, V. (2013b). Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Eng. Appl. Artif. Intel., doi:10.1016/j.engappai.2012.02.016.
Rao, R.V. & Patel, V. (2013c). Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl. Math. Model., doi.org/10.1016/j.apm.2012.03.043.
Srinivasan, D. & Seow, T.H. (2003). Particle swarm inspired evolutionary algorithm (ps-ea) for multi-objective optimization problem. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2292–2297.
Sindhya, K., Sinha, A., Deb, K. & Miettinen, K. (2009). Local search based evolutionary multi-objective optimization algorithm for constrained & unconstrained problems. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2919–2926.
Tiwari, S., Fadel, G., Koch, P. & Deb, K. (2009). Performance assessment of the hybrid archive-based micro genetic algorithm on the CEC09 test problems. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1935-1942.
Tseng, L.Y. & Chen, C. (2009). Multiple trajectory search for unconstrained/constrained multi-objective optimization. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1951–1958.
Van Veldhuizen, D.A. (1999). Multi-objective evolutionary algorithms: classifications, analyses & new Innovations. Evol. Comput., 8(2), 125-147.
Wang, Y., Dang, C., Li, H., Han, L. & Wei, J. (2009). A clustering multi-objective evolutionary algorithm based on orthogonal & uniform design. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2927–2933.
Yen, G.G. & Leong, W.F. (2009). Dynamic multiple swarms in multi-objective particle swarm optimization. IEEE T. Syst. Man Cy. A, 39(4), 1013–1027.
Zamuda, A., Brest, J., Boskovic, B. & Zumer, V. (2009). Differential evolution with self adaptation & local search for constrained multi-objective optimization. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 192–202.
Zhang, Q., Liu, W. & Li, H. (2009). The performance of a new version of MOEA/D on CEC09 unconstrained mop test instances. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 203–208.
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W. & Tiwari, S. (2009). Multi-objective optimization test instances for the congress on evolutionary computation (CEC 2009) special session & competition. Working Report CES-887. University of Essex, UK.
Zeng, F., Decraene, J., Low, M.Y.H., Hingston, P., Wentong, C., Suiping, Z. & Ch & ramohan, M. (2010). Autonomous bee colony optimization for multi-objective function. In: 2010 IEEE Congress on Evolutionary Computation, 18-23 July, Barcelona, Spain, 1–8.
Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N. & Zhang Q. (2011). Multi-objective evolutionary algorithms: a survey of the state-of-the-art. Swarm & Evolutionary Computation, 1(1), 32–49.
Zou, W., Zhu, Y., Chen, H. & Shen, H. (2011). A novel multi-objective optimization algorithm based on artificial bee colony. In: Genetic & Evolutionary Computation Conference (GECCO’11), 12-16 July, Dublin, Ireland, 103–104.
Akbari, R. & Ziarati , K. (2012). Multi-objective bee swarm optimization. Int. J. Innov. Comput. I., 8 (1-B), 715-726.
Chen, C.M., Chen, Y. & Zhang, Q. (2009). Enhancing MOEA/D with guided mutation & priority update for multi-objective optimization. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 209–216.
Coello Coello, C.A., Lamont, G.B. & Van Veldhuizen, D.A., Evolutionary Algorithms for Solving Multi-Objective Problems. Springer-Verlag (2007).
Coello Coello, C.A., Pulido ,G.T. & Lechuga, M.S. (2004). H & ling multiple objectives with particle swarm optimization. IEEE T. Evolut. Comput., 8(3), 256-279.
Deb, K., Mohan, M. & Mishra, S. (2005) “Evaluating the epsilon-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions”, Evol. Comput.,13(4), 501–525.
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast & elitist multi-objective genetic algorithm: NSGA-II. IEEE T. Evolut. Comput., 6(2), 182-197.
Huang, V.L., Zhao, S.Z., Mallipeddi, R. & Suganthan, P.N. (2009). Multi-objective optimization using self-adaptive differential evolution algorithm. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 190–194.
Liu, H. & Li, X. (2009). The multi-objective evolutionary algorithm based on determined weight & sub-regional search. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1928–1934.
Liu, M., Zou, X., Chen, Y. & Wu, Z. (2009). Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances” In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2913-2918.
Gao, S., Zeng, S., Xiao, B., Zhang, L., Shi, Y., Tian, X., Yang, Y., Long, H., Yang, X., Yu, D. & Yan, Z. (2009). An orthogonal multi-objective evolutionary algorithm with lower-dimensional crossover. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1959–1964.
Hedayatzadeh, R,, Hasanizadeh, B., Akbari, R. & Ziarati, K. (2010). A multi-objective artificial bee colony for optimizing multi-objective problems. In: 3rd International Conference on Advanced Computer Theory & Engineering (ICACTE ), 5, 271–281.
Karaboga, D., Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm, Appl. Math. Comput. 214, 108–132.
Kukkonen, S. & Lampinen, J. (2009). Performance assessment of generalized differential evolution with a given set of constrained multi-objective test problems. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1943–1950.
Leong, W.F. & Yen, G.G. (2008). PSO-based multi-objective optimization with dynamic population size & adaptive local archives. IEEE T. Syst. Man Cy. B, 38(5), 1270–1293.
Mostaghim, S. & Teich, J. (2004). Covering Pareto-optimal fronts by sub swarms in multi-objective particle swarm optimization. In: 2004 IEEE Congress on Evolutionary Computation, 19-23 June, Portl & , USA, 1404–1411.
Qu, B.Y. & Suganthan, P.N. (2011). Constrained multi-objective optimization algorithm with ensemble of constraint h & ling methods. Eng. Optimiz., 43(4), 403-434.
Qu, B.Y. & Suganthan, P.N. (2009). Multi-objective evolutionary programming without non-domination sorting is up to twenty times faster. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2934–2939.
Rao, R.V., Savsani, V.J. & Vakharia, D.P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput. Aided Design, 43(3), 303-315.
Rao, R.V., Savsani, V.J. & Vakharia, D.P. (2012). Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems. Inform. Sciences, 183(1), 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. (2013a). Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. International Journal of Industrial Engineering Computations, doi: 10.5267/j.ijiec.2012.09.001.
Rao, R.V. & Patel, V. (2013b). Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Eng. Appl. Artif. Intel., doi:10.1016/j.engappai.2012.02.016.
Rao, R.V. & Patel, V. (2013c). Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl. Math. Model., doi.org/10.1016/j.apm.2012.03.043.
Srinivasan, D. & Seow, T.H. (2003). Particle swarm inspired evolutionary algorithm (ps-ea) for multi-objective optimization problem. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2292–2297.
Sindhya, K., Sinha, A., Deb, K. & Miettinen, K. (2009). Local search based evolutionary multi-objective optimization algorithm for constrained & unconstrained problems. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2919–2926.
Tiwari, S., Fadel, G., Koch, P. & Deb, K. (2009). Performance assessment of the hybrid archive-based micro genetic algorithm on the CEC09 test problems. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1935-1942.
Tseng, L.Y. & Chen, C. (2009). Multiple trajectory search for unconstrained/constrained multi-objective optimization. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 1951–1958.
Van Veldhuizen, D.A. (1999). Multi-objective evolutionary algorithms: classifications, analyses & new Innovations. Evol. Comput., 8(2), 125-147.
Wang, Y., Dang, C., Li, H., Han, L. & Wei, J. (2009). A clustering multi-objective evolutionary algorithm based on orthogonal & uniform design. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 2927–2933.
Yen, G.G. & Leong, W.F. (2009). Dynamic multiple swarms in multi-objective particle swarm optimization. IEEE T. Syst. Man Cy. A, 39(4), 1013–1027.
Zamuda, A., Brest, J., Boskovic, B. & Zumer, V. (2009). Differential evolution with self adaptation & local search for constrained multi-objective optimization. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 192–202.
Zhang, Q., Liu, W. & Li, H. (2009). The performance of a new version of MOEA/D on CEC09 unconstrained mop test instances. In: 2009 IEEE Congress on Evolutionary Computation, 18-21 May, Trondheim, Norway, 203–208.
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W. & Tiwari, S. (2009). Multi-objective optimization test instances for the congress on evolutionary computation (CEC 2009) special session & competition. Working Report CES-887. University of Essex, UK.
Zeng, F., Decraene, J., Low, M.Y.H., Hingston, P., Wentong, C., Suiping, Z. & Ch & ramohan, M. (2010). Autonomous bee colony optimization for multi-objective function. In: 2010 IEEE Congress on Evolutionary Computation, 18-23 July, Barcelona, Spain, 1–8.
Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N. & Zhang Q. (2011). Multi-objective evolutionary algorithms: a survey of the state-of-the-art. Swarm & Evolutionary Computation, 1(1), 32–49.
Zou, W., Zhu, Y., Chen, H. & Shen, H. (2011). A novel multi-objective optimization algorithm based on artificial bee colony. In: Genetic & Evolutionary Computation Conference (GECCO’11), 12-16 July, Dublin, Ireland, 103–104.