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Title: Optimised scheduling of network teaching resource management based on improved genetic algorithm

Authors: Jing Zhang

Addresses: School of Engineering, Changchun Normal University, Changchun, 400056, China

Abstract: Network teaching resources provide convenience for daily teaching. Intending to issues of low scheduling accuracy and long response time in management methods of network teaching resources, this paper first optimises the genetic algorithm (GA) based on adaptive neighbourhood and wolf swarm algorithm. First, through the optimisation of encoding and initial population, an adaptive crossover operation based on greedy algorithm is designed. The reversal operation and variable neighbourhood search algorithm are used to complete the mutation operation of the population. Then, a mathematical model of network teaching resource scheduling is established, and the improved GA is used to solve the mathematical model, thereby obtaining the list of network teaching resource information after optimisation scheduling. Experimental results show that the management scheduling accuracy of the proposed method is 98.45%, and the response time is 0.23 s, providing a feasible solution for the intelligent scheduling of network teaching platforms.

Keywords: network teaching resources; management optimisation scheduling; genetic algorithm; GA; adaptive neighbourhood; wolf swarm algorithm.

DOI: 10.1504/IJICT.2025.149815

International Journal of Information and Communication Technology, 2025 Vol.26 No.40, pp.70 - 85

Received: 29 Jul 2025
Accepted: 17 Sep 2025
Published online: 13 Nov 2025 *