Abstract
Teaching-learning-based Optimization (TLBO) is a popular meta-heuristic optimisation method that has been used in solving a number of scientific and engineering problems. In this paper, a new variant, namely Teaching-learning-feedback-based Optimization (TLFBO) is proposed. In addition to the two phases in the canonical TLBO, an additional feedback learning phase is employed to further speed up the convergence. The teacher in the previous generation is recorded and communicates with the current teacher to provide combined feedbacks to the learners and supervise the learning direction to avoid wasting computational efforts incurred in the previous generations. Numerical experiments on 10 well-known benchmark functions are conducted to evaluate the performance of the TLFBO, and experimental results show that the proposed TLFBO has a superior and competitive capability in solving continuous optimisation problems.
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Chowdhury, P.R., Singh, Y.P., Chansarkar, R.A.: Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 30(3), 384–390 (2000)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)
Gao, W.F., Huang, L.L., Liu, S.Y., Dai, C.: Artificial bee colony algorithm based on information learning. IEEE Trans. Cybern. 45(12), 2827–2839 (2015)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995
Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Rao, R., Patel, V.: Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Int. J. Ind. Eng. Comput. 4(1), 29–50 (2013)
Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput.-Aided Des. 43(3), 303–315 (2011)
Rao, R., Savsani, V.J., Vakharia, D.: Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf. Sci. 183(1), 1–15 (2012)
Satapathy, S.C., Naik, A.: Modified teaching-learning-based optimization algorithm for global numerical optimizationa comparative study. Swarm Evol. Comput. 16, 28–37 (2014)
Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998). doi:10.1007/BFb0040810
Wang, Z., Lu, R., Chen, D., Zou, F.: An experience information teaching-learning-based optimization for global optimization. IEEE Trans. Syst. Man Cybern.: Syst. 46(9), 1202–1214 (2016)
Yang, Z., Li, K., Foley, A., Zhang, C.: A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2685–2691 (2014)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)
Zhile, Y., Kang, L., Qun, N., Yusheng, X., Foley, A.: A self-learning tlbo based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads. J. Mod. Power Syst. Clean Energy 2(4), 298–307 (2014)
Acknowledgment
This paper was partially funded by the EPSRC under grant EP/P004636/1 and partially supported by NSFC under 61673256, and Shanghai Science Technology Commission under grant No. 14ZR1414800.
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Li, X., Li, K., Yang, Z. (2017). Teaching-Learning-Feedback-Based Optimization. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_8
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DOI: https://doi.org/10.1007/978-3-319-61824-1_8
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