How to cite this paper
Rai, D. (2017). Comments on “A note on multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO)”.International Journal of Industrial Engineering Computations , 8(2), 179-190.
Refrences
Chinta, S., Kommadath, R. & Kotecha, P. (2016). A note on multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Information Science, 373, 337-350.
Deb, K., Mohan, M., & Mishra, S. (2005). Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary computation, 13(4), 501-525.
Li, D., Zhang, C., Shao, X., & Lin, W. (2014). A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. Journal of Intelligent Manufacturing, 27(4), 725-739.
Medina, M.A., Das, S., Coello, C.A.C. & Ramírez, J.M. (2014). Decomposition-based modern metaheuristic algorithms for multi- objective optimal power flow—A comparative study. Engineering Applications of Artificial Intelligence, 32, 10–20.
Mernik, M., Liu, S.H., Karaboga, D. & Crepinsek, M. (2015). On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation. Information Sciences, 291, 115–127.
Patel, V. & Savsani, V.J. (2016). A multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Information Science, 357, 182–200.
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. (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. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720.
Rao, R.V., & 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.
Rao, R.V. (2016a). Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decision Science Letters, 5(1), 1-30.
Rao, R.V. (2016b). Teaching–learning-based optimization (TLBO) algorithm and its engineering applications. Switzerland: Springer International Publishing.
Rao, R.V., Rai, D.P. & Balic, J. (2016) Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm. Journal of Intelligent Manufacturing. DOI 10.1007/s10845-016-1210-5
Sultana, S., & Roy, P. K. (2014). Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Electrical Power and Energy Systems, 63, 534–535.
Yu, K., Wang, X., & Wang, Z. (2015). Self-adaptive multi-objective teaching–learning-based optimization and its application in ethylene cracking furnace operation optimization. Chemometrics and Intelligent Laboratory Systems, 146, 198–210.
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.
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, F., Wang, L., Hei, X., Chen, D. & Wang, B. (2013). Multi-objective optimization using teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26, 1291–1300.
Deb, K., Mohan, M., & Mishra, S. (2005). Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary computation, 13(4), 501-525.
Li, D., Zhang, C., Shao, X., & Lin, W. (2014). A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. Journal of Intelligent Manufacturing, 27(4), 725-739.
Medina, M.A., Das, S., Coello, C.A.C. & Ramírez, J.M. (2014). Decomposition-based modern metaheuristic algorithms for multi- objective optimal power flow—A comparative study. Engineering Applications of Artificial Intelligence, 32, 10–20.
Mernik, M., Liu, S.H., Karaboga, D. & Crepinsek, M. (2015). On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation. Information Sciences, 291, 115–127.
Patel, V. & Savsani, V.J. (2016). A multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Information Science, 357, 182–200.
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. (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. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720.
Rao, R.V., & 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.
Rao, R.V. (2016a). Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decision Science Letters, 5(1), 1-30.
Rao, R.V. (2016b). Teaching–learning-based optimization (TLBO) algorithm and its engineering applications. Switzerland: Springer International Publishing.
Rao, R.V., Rai, D.P. & Balic, J. (2016) Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm. Journal of Intelligent Manufacturing. DOI 10.1007/s10845-016-1210-5
Sultana, S., & Roy, P. K. (2014). Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Electrical Power and Energy Systems, 63, 534–535.
Yu, K., Wang, X., & Wang, Z. (2015). Self-adaptive multi-objective teaching–learning-based optimization and its application in ethylene cracking furnace operation optimization. Chemometrics and Intelligent Laboratory Systems, 146, 198–210.
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.
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, F., Wang, L., Hei, X., Chen, D. & Wang, B. (2013). Multi-objective optimization using teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26, 1291–1300.