CTLBO: Converged teaching–learning–based optimization

References

• Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Proceedings of IEEE Congress on Evolutionary Computation, Singapore.
   Google Scholar
• Back, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. New York, NY: Oxford University Press.
   Google Scholar
• Chen, L., Xiao, C., Li, X., Wang, Z., & Huo, S. (2018). A seismic fault recognition method based on ant colony optimization. Journal of Applied Geophysics, 152, 1–14. doi:10.1016/j.jappgeo.2018.02.009
   Web of Science ®Google Scholar
• Dorigo, M. (1992). Optimization, learning and natural algorithms ( PhD thesis). Politecnico di Milano, Italy.
   Google Scholar
• Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76, 60–68. doi:10.1177/003754970107600201
   Google Scholar
• Holland, J. (1975). Adaptation in natural and artificial systems: An introductory analysis with application to biology. Control and Artificial Intelligence. England: University of Michigan Press.
   Google Scholar
• Karaboga, D. (2010). Artificial bee colony algorithm. Scholarpedia, 5(3), 6915. doi:10.4249/scholarpedia.6915
   Google Scholar
• Karaboga, D., & Akay, B. 2009. Artificial bee colony (ABC), harmony search and bees algorithms on numerical optimization. In Innovative Production Machines and Systems Virtual Conference, Cardiff, UK.
   Google Scholar
• Kashan, A. H. (2014). League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships (Vol. 16, pp. 171–200). Applied Soft Computing. doi: 10.1016/j.asoc.2013.12.005
   Google Scholar
• Kennedy, J. (2011). Particle swarm optimization. In C. Sammut, & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 760–766). Boston, MA: Springer.
   Google Scholar
• Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295. doi:10.1109/TEVC.2005.857610
   Web of Science ®Google Scholar
• Ma, L., Zhu, Y., Liu, Y., Tian, L., & Chen, H. (2015). A novel bionic algorithm inspired by plant root foraging behaviors. Applied Soft Computing, 37, 95–113. doi:10.1016/j.asoc.2015.08.014
   Web of Science ®Google Scholar
• Mahmoodabadi, M. J., & Bisheban, M. (2014). An online optimal linear state feedback controller based on MLS approximations and a novel straightforward PSO algorithm. Transactions of the Institute of Measurement and Control, 36(8), 1132–1142. doi:10.1177/0142331214537014
   Web of Science ®Google Scholar
• Mahmoodabadi, M. J., Rasekh, M., & Zohari, T. (2018). TGA: Team game algorithm. Future Computing and Informatics Journal, 3(2), 191–199. doi:10.1016/j.fcij.2018.03.002
   Google Scholar
• Mahmoodabadi, M. J., Taherkhorsandi, M., & Bagheri, A. (2014). Optimal robust sliding mode tracking control of a biped robot based on ingenious multi-objective PSO. Neurocomputing, 124, 194–209. doi:10.1016/j.neucom.2013.07.009
   Web of Science ®Google Scholar
• Mahmoodabadi, M. J., & Ziaei, A. (2019). Inverse dynamics based optimal fuzzy controller for a robot manipulator via particle swarm optimization. Journal of Robotics, 2019, 1–10. doi:10.1155/2019/5052185
   Web of Science ®Google Scholar
• Parrott, D., & Li, X. D. (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Transactions on Evolutionary Computation, 10(4), 440–458. doi:10.1109/TEVC.2005.859468
   Web of Science ®Google Scholar
• Rai, D. P. (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. doi:10.5267/j.ijiec.2016.11.002
   Google Scholar
• Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710–720.
   Web of Science ®Google Scholar
• Rao, R. V., Savsani, V. J., & Vakharia, D. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. doi:10.1016/j.cad.2010.12.015
   Web of Science ®Google Scholar
• Rao, R. V., Savsani, V. J., & Vakharia, D. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1–15. doi:10.1016/j.ins.2011.08.006
   Web of Science ®Google Scholar
• Satapathy, S. C., Naik, A., & Parvathi, K. (2013a). A teaching learning based optimization based on orthogonal design for solving global optimization problems. Springer Plus, 2(1), 130. doi:10.1186/2193-1801-2-130
   PubMedGoogle Scholar
• Satapathy, S. C., Naik, A., & Parvathi, K. (2013b). Weighted teaching-learning-based optimization for global function optimization. Applied Mathematics, 4(3), 429. doi:10.4236/am.2013.43064
   Google Scholar
• Venkata Rao, R. (2016). Teaching learning based optimization algorithm and its engineering applications. Switzerland: Springer. ISBN: 9783319227313.
   Google Scholar