How to cite this paper
Jangir, P., Manoharan, P., Pandya, S & Sowmya, R. (2023). MaOTLBO: Many-objective teaching-learning-based optimizer for control and monitoring the optimal power flow of modern power systems.International Journal of Industrial Engineering Computations , 14(2), 293-308.
Refrences
Azizipanah-Abarghooee, R., Narimani, M. R., Bahmani-Firouzi, B., & Niknam, T. (2014). Modified shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices. Journal of Intelligent & Fuzzy Systems, 26(2), 681–692.
Bader, J., & Zitzler, E. (2011). HypE: An algorithm for fast hypervolume-based many-objective optimization. Evolutionary Computation, 19(1), 45–76.
Beume, N., Naujoks, B., & Emmerich, M. (2007). SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3), 1653–1669.
Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A., & El-Sehiemy, R. A. (2016). Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Applied Soft Computing, 42, 119–131.
Capitanescu, F., & Wehenkel, L. (2013). Experiments with the interior-point method for solving large-scale Optimal Power Flow problems. Electric Power Systems Research, 95, 276–283.
Chaib, A. E., Bouchekara, H. R. E. H., Mehasni, R., & Abido, M. A. (2016). Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. International Journal of Electrical Power & Energy Systems, 81, 64–77.
Das, I., & Dennis, J. E. (1998). Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems. SIAM Journal on Optimization, 8(3), 631–657.
Davoodi, E., Babaei, E., & Mohammadi-ivatloo, B. (2018). An efficient covexified SDP model for multi-objective optimal power flow. International Journal of Electrical Power and Energy Systems, 102, 254–264.
de Carvalho, E. P., dos Santos, A., & Ma, T. F. (2008). Reduced gradient method combined with augmented Lagrangian and barrier for the optimal power flow problem. Applied Mathematics and Computation, 200(2), 529–536.
Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Ghasemi, M., Ghavidel, S., Akbari, E., & Vahed, A. A. (2014). Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy, 73, 340–353.
Gonzalez-Alvarez, D. L., Vega-Rodriguez, M. A., Gomez-Pulido, J. A., & Sanchez-Perez, J. M. (2012). Multiobjective Teaching-Learning-Based Optimization (MO-TLBO) for motif finding. CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings, 141–146.
He, X., Wang, W., Jiang, J., & Xu, L. (2015). An Improved Artificial Bee Colony Algorithm and its Application to Multi-Objective Optimal Power Flow. Energies, 8(4), 2412–2437.
Jain, H., & Deb, K. (2014). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18(4), 602–622.
Jangir, P., & Jangir, N. (2018). A new Non-Dominated Sorting Grey Wolf Optimizer (NS-GWO) algorithm: Development and application to solve engineering designs and economic constrained emission dispatch problem with integration of wind power. Engineering Applications of Artificial Intelligence, 72, 449–467.
Kumar, S., Jangir, P., Tejani, G. G., & Premkumar, M. (2022). A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization. Knowledge-Based Systems, 253, 109591.
Lashkar Ara, A., Kazemi, A., Gahramani, S., & Behshad, M. (2012). Optimal reactive power flow using multi-objective mathematical programming. Scientia Iranica, 19(6), 1829–1836.
Liu, X., & Xu, W. (2010). Minimum emission dispatch constrained by stochastic wind power availability and cost. IEEE Transactions on Power Systems, 25(3), 1705–1713.
Liu, Y., Wei, J., Li, X., & Li, M. (2019). Generational Distance Indicator-Based Evolutionary Algorithm with an Improved Niching Method for Many-Objective Optimization Problems. IEEE Access, 7, 63881–63891.
Mirjalili, S., Jangir, P., Mirjalili, S. Z., Saremi, S., & Trivedi, I. N. (2017). Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowledge-Based Systems, 134, 50–71.
Momoh, J. A., El-Hawary, M. E., & Adapa, R. (1999). A review of selected optimal power flow literature to 1993 part i: nonlinear and quadratic Programming Approaches. IEEE Transactions on Power Systems, 14(1), 96–103.
Monoh, J. A., Ei-Hawary, M. E., & Adapa, R. (1999). A review of selected optimal power flow literature to 1993 part ii: newton, linear programming and Interior Point Methods. IEEE Transactions on Power Systems, 14(1), 105–111.
Premkumar, M., Jangir, P., Sowmya, R., & Elavarasan, R. M. (2021). Many-Objective Gradient-Based Optimizer to Solve Optimal Power Flow Problems: Analysis and Validations. Engineering Applications of Artificial Intelligence, 106, 104479.
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. (2012). Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1–15.
Robert, O., Miran, B., & Borut, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations, 11(3), 359-376.
Rosehart, W. D., Cañizares, C. A., & Quintana, V. H. (2003). Multiobjective optimal power flows to evaluate voltage security costs in power networks. IEEE Transactions on Power Systems, 18(2), 578–587.
Salgado, R. S., & Rangel, E. L. (2012). Optimal power flow solutions through multi-objective programming. Energy, 42(1), 35–45.
Sandeep, U. M., & Narsingrao, M. R. (2021). A chaotic-based improved many-objective Jaya algorithm for many-objective optimization problems. International Journal of Industrial Engineering Computations, 12(1), 49-62.
Santos, A., & da Costa, G. R. M. (1995). Optimal-power-flow solution by Newton’s method applied to an augmented Lagrangian function. IEE Proceedings: Generation, Transmission and Distribution, 142(1), 33–36.
Surender Reddy, S., & Bijwe, P. R. (2019). Differential evolution-based efficient multi-objective optimal power flow. Neural Computing and Applications, 31, 509–522.
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A matlab platform for evolutionary multi-objective optimization. IEEE Computational Intelligence Magazine, 12(4), 73–87.
Trivedi, I. N., Jangir, P., Parmar, S. A., & Jangir, N. (2018). Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer. Neural Computing and Applications, 30(6), 1889–1904.
Wang, S., Zhou, Y., & Zhang, J. (2018). An Improved NSGA-III Approach to Many-Objective Optimal Power Flow Problems. 2018 Chinese Automation Congress (CAC), 2664–2669.
Yalcinoz, T., & Köksoy, O. (2007). A multiobjective optimization method to environmental economic dispatch. International Journal of Electrical Power and Energy Systems, 29(1), 42–50.
Yuan, X., Zhang, B., Wang, P., Liang, J., Yuan, Y., Huang, Y., & Lei, X. (2017). Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm. Energy, 122, 70–82.
Zhang, J., Wang, S., Tang, Q., Zhou, Y., & Zeng, T. (2019). An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems. Energy, 172, 945–957.
Zhang, J., Zhu, X., & Li, P. (2020). MOEA/D with many-stage dynamical resource allocation strategy to solution of many-objective OPF problems. International Journal of Electrical Power and Energy Systems, 120, 106050.
Zitzler, E., & Künzli, S. (2004). Indicator-based selection in multiobjective search. Lecture Notes in Computer Science, 3242, 832–842.
Bader, J., & Zitzler, E. (2011). HypE: An algorithm for fast hypervolume-based many-objective optimization. Evolutionary Computation, 19(1), 45–76.
Beume, N., Naujoks, B., & Emmerich, M. (2007). SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3), 1653–1669.
Bouchekara, H. R. E. H., Chaib, A. E., Abido, M. A., & El-Sehiemy, R. A. (2016). Optimal power flow using an Improved Colliding Bodies Optimization algorithm. Applied Soft Computing, 42, 119–131.
Capitanescu, F., & Wehenkel, L. (2013). Experiments with the interior-point method for solving large-scale Optimal Power Flow problems. Electric Power Systems Research, 95, 276–283.
Chaib, A. E., Bouchekara, H. R. E. H., Mehasni, R., & Abido, M. A. (2016). Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm. International Journal of Electrical Power & Energy Systems, 81, 64–77.
Das, I., & Dennis, J. E. (1998). Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems. SIAM Journal on Optimization, 8(3), 631–657.
Davoodi, E., Babaei, E., & Mohammadi-ivatloo, B. (2018). An efficient covexified SDP model for multi-objective optimal power flow. International Journal of Electrical Power and Energy Systems, 102, 254–264.
de Carvalho, E. P., dos Santos, A., & Ma, T. F. (2008). Reduced gradient method combined with augmented Lagrangian and barrier for the optimal power flow problem. Applied Mathematics and Computation, 200(2), 529–536.
Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Ghasemi, M., Ghavidel, S., Akbari, E., & Vahed, A. A. (2014). Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy, 73, 340–353.
Gonzalez-Alvarez, D. L., Vega-Rodriguez, M. A., Gomez-Pulido, J. A., & Sanchez-Perez, J. M. (2012). Multiobjective Teaching-Learning-Based Optimization (MO-TLBO) for motif finding. CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings, 141–146.
He, X., Wang, W., Jiang, J., & Xu, L. (2015). An Improved Artificial Bee Colony Algorithm and its Application to Multi-Objective Optimal Power Flow. Energies, 8(4), 2412–2437.
Jain, H., & Deb, K. (2014). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18(4), 602–622.
Jangir, P., & Jangir, N. (2018). A new Non-Dominated Sorting Grey Wolf Optimizer (NS-GWO) algorithm: Development and application to solve engineering designs and economic constrained emission dispatch problem with integration of wind power. Engineering Applications of Artificial Intelligence, 72, 449–467.
Kumar, S., Jangir, P., Tejani, G. G., & Premkumar, M. (2022). A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization. Knowledge-Based Systems, 253, 109591.
Lashkar Ara, A., Kazemi, A., Gahramani, S., & Behshad, M. (2012). Optimal reactive power flow using multi-objective mathematical programming. Scientia Iranica, 19(6), 1829–1836.
Liu, X., & Xu, W. (2010). Minimum emission dispatch constrained by stochastic wind power availability and cost. IEEE Transactions on Power Systems, 25(3), 1705–1713.
Liu, Y., Wei, J., Li, X., & Li, M. (2019). Generational Distance Indicator-Based Evolutionary Algorithm with an Improved Niching Method for Many-Objective Optimization Problems. IEEE Access, 7, 63881–63891.
Mirjalili, S., Jangir, P., Mirjalili, S. Z., Saremi, S., & Trivedi, I. N. (2017). Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowledge-Based Systems, 134, 50–71.
Momoh, J. A., El-Hawary, M. E., & Adapa, R. (1999). A review of selected optimal power flow literature to 1993 part i: nonlinear and quadratic Programming Approaches. IEEE Transactions on Power Systems, 14(1), 96–103.
Monoh, J. A., Ei-Hawary, M. E., & Adapa, R. (1999). A review of selected optimal power flow literature to 1993 part ii: newton, linear programming and Interior Point Methods. IEEE Transactions on Power Systems, 14(1), 105–111.
Premkumar, M., Jangir, P., Sowmya, R., & Elavarasan, R. M. (2021). Many-Objective Gradient-Based Optimizer to Solve Optimal Power Flow Problems: Analysis and Validations. Engineering Applications of Artificial Intelligence, 106, 104479.
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. (2012). Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1–15.
Robert, O., Miran, B., & Borut, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations, 11(3), 359-376.
Rosehart, W. D., Cañizares, C. A., & Quintana, V. H. (2003). Multiobjective optimal power flows to evaluate voltage security costs in power networks. IEEE Transactions on Power Systems, 18(2), 578–587.
Salgado, R. S., & Rangel, E. L. (2012). Optimal power flow solutions through multi-objective programming. Energy, 42(1), 35–45.
Sandeep, U. M., & Narsingrao, M. R. (2021). A chaotic-based improved many-objective Jaya algorithm for many-objective optimization problems. International Journal of Industrial Engineering Computations, 12(1), 49-62.
Santos, A., & da Costa, G. R. M. (1995). Optimal-power-flow solution by Newton’s method applied to an augmented Lagrangian function. IEE Proceedings: Generation, Transmission and Distribution, 142(1), 33–36.
Surender Reddy, S., & Bijwe, P. R. (2019). Differential evolution-based efficient multi-objective optimal power flow. Neural Computing and Applications, 31, 509–522.
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A matlab platform for evolutionary multi-objective optimization. IEEE Computational Intelligence Magazine, 12(4), 73–87.
Trivedi, I. N., Jangir, P., Parmar, S. A., & Jangir, N. (2018). Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer. Neural Computing and Applications, 30(6), 1889–1904.
Wang, S., Zhou, Y., & Zhang, J. (2018). An Improved NSGA-III Approach to Many-Objective Optimal Power Flow Problems. 2018 Chinese Automation Congress (CAC), 2664–2669.
Yalcinoz, T., & Köksoy, O. (2007). A multiobjective optimization method to environmental economic dispatch. International Journal of Electrical Power and Energy Systems, 29(1), 42–50.
Yuan, X., Zhang, B., Wang, P., Liang, J., Yuan, Y., Huang, Y., & Lei, X. (2017). Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm. Energy, 122, 70–82.
Zhang, J., Wang, S., Tang, Q., Zhou, Y., & Zeng, T. (2019). An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems. Energy, 172, 945–957.
Zhang, J., Zhu, X., & Li, P. (2020). MOEA/D with many-stage dynamical resource allocation strategy to solution of many-objective OPF problems. International Journal of Electrical Power and Energy Systems, 120, 106050.
Zitzler, E., & Künzli, S. (2004). Indicator-based selection in multiobjective search. Lecture Notes in Computer Science, 3242, 832–842.