@Article{2018.100000042,
AUTHOR = {Qinqin Fan, Yilian Zhang, Zhihuan Wang},
TITLE = {Improved Teaching Learning Based Optimization and Its Application in  Parameter Estimation of Solar Cell Models},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {1},
PAGES = {1--12},
URL = {http://www.techscience.com/iasc/v26n1/39840},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.31209/2018.100000042}
}