Adaptive optimisation algorithm for online teaching behaviour

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.