[1] H. Holland John, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, USA: University of Michigan, (1975).
[2] R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization, 11(4) (1997) 341-359.
[3] J.D. Farmer, N.H. Packard, A.S. Perelson, The immune system, adaptation, and machine learning, Physica D: Nonlinear Phenomena, 22(1-3) (1986) 187-204.
[4] K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, IEEE control systems, 22(3) (2002) 52-67.
[5] J. Kennedy, R. Eberhart, PSO optimization, in: Proc. IEEE Int. Conf. Neural Networks, IEEE Service Center, Piscataway, NJ, 1995, pp. 1941-1948.
[6] M. Dorigo, V. Maniezzo, A. Colorni, Positive feedback as a search strategy. Dipartimento di Elettronica, Politecnico di Milano, Italy, Tech. Rep. 91-016, 1991.
[7] M.M. Eusuff, K.E. Lansey, Optimization of water distribution network design using the shuffled frog leaping algorithm, Journal of Water Resources planning and management, 129(3) (2003) 210-225.
[8] D. Karaboga, An idea based on honey bee swarm for numerical optimization, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, 2005.
[9] A.F. Nematollahi, A. Rahiminejad, B.J.A.S.C. Vahidi, A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization, 59 (2017) 596-621.
[10] T. Back, Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, Oxford university press, 1996.
[11] R.V. Rao, V. Savsani, J. Balic, Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems, Engineering Optimization, 44(12) (2012) 1447-1462.
[12] R. Venkata Rao, V. Patel, Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms, Engineering Optimization, 44(8) (2012) 965-983.
[13] S.C. Satapathy, A. Naik, Data clustering based on teaching-learning-based optimization, in: International Conference on Swarm, Evolutionary, and Memetic Computing, Springer, 2011, pp. 148-156.
[14] V. Toğan, Design of planar steel frames using teaching–learning based optimization, Engineering Structures, 34 (2012) 225-232.
[15] R. Venkata Rao, V. Kalyankar, Parameter optimization of machining processes using a new optimization algorithm, Materials and Manufacturing Processes, 27(9) (2012) 978-985.
[16] R.V. Rao, V.J. Savsani, D. Vakharia, Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems, Information Sciences, 183(1) (2012) 1-15.
[17] S.C. Satapathy, A. Naik, K. Parvathi, Unsupervised feature selection using rough set and teaching learning-based optimisation, International Journal of Artificial Intelligence and Soft Computing, 3(3) (2013) 244-256.
[18] S.C. Satapathy, A. Naik, K. Parvathi, Unsupervised feature selection using rough set and teaching learning-based optimisation, International Journal of Artificial Intelligence and Soft Computing, 3(3) (2013) 244-256.
[19] S.C. Satapathy, A. Naik, K. Parvathi, Weighted teaching-learning-based optimization for global function optimization, Applied Mathematics, 4(03) (2013) 429.
[20] S.C. Satapathy, A. Naik, K. Parvathi, A teaching learning based optimization based on orthogonal design for solving global optimization problems, SpringerPlus, 2(1) (2013) 130.
[21] D. Chen, F. Zou, Z. Li, J. Wang, S. Li, An improved teaching–learning-based optimization algorithm for solving global optimization problem, Information Sciences, 297 (2015) 171-190.
[22] S.C. Satapathy, A. Naik, Modified Teaching–Learning-Based Optimization algorithm for global numerical optimization—A comparative study, Swarm and Evolutionary Computation, 16 (2014) 28-37.
[23] R.V. Rao, V. Patel, An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems, Scientia Iranica, 20(3) (2013) 710-720.
[24] B. Mandal, P.K. Roy, Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization, International Journal of Electrical Power & Energy Systems, 53 (2013) 123-134.
[25] N. Khalesi, N. Rezaei, M.-R. Haghifam, DG allocation with application of dynamic programming for loss reduction and reliability improvement, International Journal of Electrical Power & Energy Systems, 33(2) (2011) 288-295.
[25] N. Khalesi, N. Rezaei, M.-R. Haghifam, DG allocation with application of dynamic programming for loss reduction and reliability improvement, International Journal of Electrical Power & Energy Systems, 33(2) (2011) 288-295.
[27] A. Forooghi Nematollahi, A. Dadkhah, O. Asgari Gashteroodkhani, B.J.J.o.R. Vahidi, S. Energy, Optimal sizing and siting of DGs for loss reduction using an iterative-analytical method, 8(5) (2016) 055301.
[28] A. Foroughi Nematollahi, A. Rahiminejad, B. Vahidi, H. Askarian, A.J.J.o.R. Safaei, S. Energy, A new evolutionary-analytical two-step optimization method for optimal wind turbine allocation considering maximum capacity, 10(4) (2018) 043312.
[29] M. Hamzeh, B. Vahidi, A.F.J.I.T.o.I.I. Nematollahi, Optimizing Configuration of Cyber Network Considering Graph Theory Structure and Teaching-Learning-Based Optimization (GT-TLBO), (2018).
[30] K. Moosavi, B. Vahidi, H. Askarian Abyaneh, A.J.J.o.R. Foroughi Nematollahi, S. Energy, Intelligent control of power sharing between parallel-connected boost converters in micro-girds, 9(6) (2017) 065504.
[31] G.I. Rashed, Y. Sun, H. Shaheen, Optimal TCSC placement in a power system by means of Differential Evolution Algorithm considering loss minimization, in: Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on, IEEE, 2011, pp. 2209-2215.
[32] R.S. Rao, K. Ravindra, K. Satish, S. Narasimham, Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation, IEEE transactions on power systems, 28(1).