Qinqin Fan1,*, Yilian Zhang2, Zhihuan Wang1
Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 1-12, 2020, DOI:10.31209/2018.100000042
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 More >
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