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Improved Teaching Learning Based Optimization and Its Application in Parameter Estimation of Solar Cell Models


Weak global exploration capability is one of the primary drawbacks in teaching learning based optimization (TLBO). To enhance the search capability of TLBO, an improved TLBO (ITLBO) is introduced in this study. In ITLBO, a uniform random number is replaced by a normal random number, and a weighted average position of the current population is chosen as the other teacher. The performance of ITLBO is compared with that of five meta-heuristic algorithms on a well-known test suite. Results demonstrate that the average performance of ITLBO is superior to that of the compared algorithms. Finally, ITLBO is employed to estimate parameters of two solar cell models. Experiments verify that ITLBO can provide competitive results.



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A. Askarzadeh & Rezazadeh, A. (2013). Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Applied Energy, 102, 943-949.

S. Banerjee, Maity, D., & Chanda, C. K. (2015). Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. International Journal of Electrical Power & Energy Systems, 73, 456-464.

S. Chatterjee & Mukherjee, V. (2016). PID controller for automatic voltage regulator using teaching-learning based optimization technique. International Journal of Electrical Power & Energy Systems, 77, 418-429.

X. Chen, Yu, K., Du, W., Zhao, W., & Liu, G. (2016). Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy, 99, 170-180.

E. Cuevas, Santuario, E., Zaldivar, D., & Perez-Cisneros, M. (2016). An improved evolutionary algorithm for reducing the number of function evaluations. Intelligent Automation & Soft Computing, 22, 177-192.

T. Easwarakhanthan, Bottin, J., Bouhouch, I., & Boutrit, C. (1986). Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. International Journal of Solar Energy, 4, 1-12.

Q. Q. Fan, Yan, X. F., & Zhang, Y. L. (2017). Auto-selection mechanism of differential evolution algorithm variants and its application. European Journal of operational research. J. Gow & Manning, C. (1999). Development of a photovoltaic array model for use in power-electronics simulation studies. In Electric Power Applications, IEE Proceedings- (Vol. 146, pp. 193-200): IET. J. H. Holland. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence: U Michigan Press. D. Karaboga. (2005). An idea based on honey bee swarm for numerical optimization. In: Technical report-tr06, Erciyes University, engineering faculty, computer engineering department.

Q. Q. Fan & Yan, X. F. (2016). Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies. IEEE transactions on cybernetics, 46, 219-232.

H. Feshki Farahani & Rashidi, F. (2017). An improved teaching-learning-based optimization with differential evolution algorithm for optimal power flow considering HVDC system. Journal of Renewable and Sustainable Energy, 9, 035505.

M. Friedman. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American statistical association, 32, 675-701.

S. García, Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms” behaviour: a case study on the CEC”2005 special session on real parameter optimization. Journal of Heuristics, 15, 617-644.

M. Ghasemi, Ghavidel, S., Gitizadeh, M., & Akbari, E. (2015). An improved teaching-learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. International Journal of Electrical Power & Energy Systems, 65, 375-384.

M. Ghasemi, Taghizadeh, M., Ghavidel, S., Aghaei, J., & Abbasian, A. (2015). Solving optimal reactive power dispatch problem using a novel teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 39, 100-108.

W. Gong & Cai, Z. (2013). Parameter extraction of solar cell models using repaired adaptive differential evolution. Solar Energy, 94, 209-220.

N. Hansen & Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, 9, 159-195.

G. Hermosilla, Ruiz-del-Solar, J., & Verschae, R. (2017). An enhanced representation of thermal faces for improving local appearance-based face recognition. Intelligent Automation & Soft Computing, 23, 1-12.

L. L. Jiang, Maskell, D. L., & Patra, J. C. (2013). Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm. Applied Energy, 112, 185-193.

J. Kenndy & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks (Vol. 4, pp. 1942-1948).

M. Li, Ma, H., & Gu, B. (2016). Improved teaching-learning-based optimization algorithm with group learning. Journal of Intelligent & Fuzzy Systems, 31, 2101-2108.

Y. L. Li, Zhan, Z. H., Gong, Y. J., Chen, W. N., Zhang, J., & Li, Y. (2015). Differential evolution with an evolution path: A DEEP evolutionary algorithm. Cybernetics, IEEE Transactions on, 45, 1798-1810.

J. J. Liang, 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, 281-295.

W. Lin, Yu, D., Wang, S., Zhang, C., Zhang, S., Tian, H., Luo, M., & Liu, S. (2015). Multi-objective teaching-learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations. Engineering Optimization, 47, 994-1007.

D. Oliva, Cuevas, E., & Pajares, G. (2014). Parameter identification of solar cells using artificial bee colony optimization. Energy, 72, 93-102.

J. Patel, Savsani, V., Patel, V., & Patel, R. (2017). Layout optimization of a wind farm to maximize the power output using enhanced teaching learning based optimization technique. Journal of Cleaner Production, 158, 81-94.

R. Rao & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3, 535-560.

R. V. Rao & Kalyankar, V. (2013). Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26, 524-531.

R. V. Rao, Savsani, V. J., & Vakharia, D. (2011). Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303-315.

P. K. Roy, Paul, C., & Sultana, S. (2014). Oppositional teaching learning based optimization approach for combined heat and power dispatch. International Journal of Electrical Power & Energy Systems, 57, 392-403.

S. C. Satapathy & Naik, A. (2014). Modified Teaching-Learning-Based Optimization algorithm for global numerical optimization—A comparative study. Swarm and Evolutionary Computation, 16, 28-37.

S. Sleesongsom & Bureerat, S. (2017). Four-bar linkage path generation through self-adaptive population size teaching-learning based optimization. Knowledge-Based Systems, 135, 180-191.

R. Storn & Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces (Vol. 3): ICSI Berkeley. P. N. Suganthan, Hansen, N., Liang, J. J., Deb, K., Chen, Y.-P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005.

Q. Tang, Li, Z., Zhang, L., & Zhang, C. (2017). Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm. Computers & Operations Research, 82, 102-113.

Z. Wang, Lu, R., Chen, D., & Zou, F. (2016). An Experience Information Teaching-Learning-Based Optimization for Global Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46, 1202-1214.

X. Wei, Wang, Y., Li, Z., Zou, T., & Yang, G. (2015). Mining Users interest navigation patterns using improved ant colony optimization. Intelligent Automation & Soft Computing, 21, 445-454.

F. Wilcoxon. (1945). Individual comparisons by ranking methods. Biometrics bulletin, 1, 80-83.

K. Z. Zamli, Din, F., Baharom, S., & Ahmed, B. S. (2017). Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites. Engineering Applications of Artificial Intelligence, 59, 35-50.

Zhao, Xiu-hong. "Improved Teaching-Learning Based Optimization for Global Optimization Problems." 2015 34th Chinese Control Conference (CCC) (2015): n. pag. Crossref. Web.

F. Zou, Wang, L., Hei, X., Chen, D., Jiang, Q., & Li, H. (2014). Bare-bones teaching-learning-based optimization. The Scientific World Journal, 2014.

F. Zou, Wang, L., Hei, X., Chen, D., & Yang, D. (2014). Teaching-learning-based optimization with dynamic group strategy for global optimization. Information Sciences, 273, 112-131.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present

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