Abstract
Teaching-learning-based optimization algorithm (TLBOA) is a significant metaheuristic algorithm. It is a proficient approach for solving multidimensional, linear, and nonlinear optimization problems. It is based on teaching-learning (TL) process that searches for a global optimum through two modules of learning: (a) teacher-phase (TP) and (b) learner-phase (LP). For avoiding the premature convergence of TLBOA, half-life teaching factor is discovered in this paper. The proposed strategy is known as half-life teaching factor based TLBO (HLTLBO) algorithm. The performance of HLTLBO is calculated over 20 benchmark functions and compared with various state-of-art algorithms namely, TLBOA, global-Best inspired biogeography-based optimization (GBBO), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMA-ES). The obtained outcomes validate the authenticity of the discovered HLTLBO.
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References
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.
Singh, G., Sharma, N., Sharma, H. (2017). Intelligent neighbourhood teaching learning based optimization algorithm. In 2017 International conference on computing, communication and automation (ICCCA) (pp. 986–991). Piscataway: IEEE.
Ghasemi, M., Ghanbarian, M.M., Ghavidel, S., Rahmani, S., Moghaddam, E.M. (2014). Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: A comparative study. Information Sciences, 278, 231–249.
Kunjie, Y., Wang, X., & Wang, Z. (2016). An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. Journal of Intelligent Manufacturing, 27(4), 831–843.
Zheng, S., & Ren, Z. (2016). Closed-loop teaching-learning-based optimization algorithm for global optimization. In 2016 12th world congress on intelligent control and automation (WCICA) (pp. 2120–2125). Piscataway: IEEE.
Sharma, P. K., Sharma, H., & Sharma, N. (2016). Gbest inspired biogeography based optimization algorithm. In IEEE international conference on power electronics, intelligent control and energy systems (ICPEICES) (pp. 1–6). Piscataway: IEEE.
Kennedy, J. (2011). Particle swarm optimization. In Encyclopedia of machine learning (pp. 760–766). Berlin: Springer.
Chocat, R., Brevault, L., Balesdent, M., & Defoort, S. (2015). Modified covariance matrix adaptation-evolution strategy algorithm for constrained optimization under uncertainty, application to rocket design. International Journal for Simulation and Multidisciplinary Design Optimization, 6, A1.
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.
Hansen, N. (2006). Compilation of results on the 2005 CEC benchmark function set. Online.
Tang, K., Yáo, X., Suganthan, P. N., MacNish, C., Chen, Y.-P., Chen, C.-M., & Yang, Z. (2007). Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Nature inspired computation and applications laboratory, USTC, China (Vol. 24).
Li, X., Tang, K., Omidvar, M. N., Yang, Z., Qin, K., & China, H. (2013). Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization. gene, 7(33), 8.
Sharma, A., Sharma, H., Bhargava, A., Sharma, N., & Bansal, J. C. (2016). Optimal power flow analysis using lévy flight spider monkey optimisation algorithm. International Journal of Artificial Intelligence and Soft Computing, 5(4), 320–352.
Sharma, N., Sharma, H., Sharma, A., & Bansal, J. C. (2016). Modified artificial bee colony algorithm based on disruption operator. In Proceedings of fifth international conference on soft computing for problem solving (pp. 889–900). Berlin: Springer.
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Mishra, R., Sharma, N., Sharma, H. (2020). Half-Life Teaching Factor Based TLBO Algorithm. In: Kolhe, M., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 94. Springer, Singapore. https://doi.org/10.1007/978-981-15-0694-9_25
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DOI: https://doi.org/10.1007/978-981-15-0694-9_25
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