Adaptive optimisation algorithm for online teaching behaviour
by Jinhua Zhu
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 31, No. 3, 2021

Abstract: It is believed that there are two mechanisms, namely, the mechanism of mutual learning among multiple teachers and the mechanism of adaptive step size improvement, to optimise teaching learning-based optimisation (TLBO) algorithm. Firstly, by setting up multiple teachers to teach in the TLBO algorithm, the diverse nature of the population can be preserved. The algorithm is improved in the precision of optimisation, and the algorithm is improved on the weakness of local optimisation. The student's learning step size is a random value in the standard algorithm, which neglects the fact that the student's progress speed changes continuously with own state. Adjusting the student's own state is an improved learning step, which can improve the accuracy of the algorithm. The results show that the improved algorithm has faster convergence speed and higher solution precision, so the improved algorithm is superior to TLBO in solution accuracy, stability and convergence speed.

Online publication date: Tue, 06-Jul-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email