Title: Adaptive optimisation algorithm for online teaching behaviour

Authors: Jinhua Zhu

Addresses: Zhejiang Yuexiu University of Foreign Languages, Shaoxing 312069, China

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.

Keywords: teaching learning-based optimisation algorithm; adaptive step size; global optimisation; multi-teacher.

DOI: 10.1504/IJCEELL.2021.115987

International Journal of Continuing Engineering Education and Life-Long Learning, 2021 Vol.31 No.3, pp.405 - 417

Received: 26 Jul 2019
Accepted: 03 Dec 2019

Published online: 27 Apr 2021 *

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