Application of Swarm Intelligence Algorithms in Higher Vocational Teaching
Qin Li
Journal of Information Security Research, Vol. 16, No. 1, 2025.
DOI: https://doi.org/10.6025/jisr/2025/16/1/17-24
Link: https://www.dline.info/jisr/fulltext/v16n1/jisrv16n1_3.pdf
Abstract:
College Chinese,â€ as a core course in literary majors, faces challenges in achieving its intended goals due to the
limitations of traditional teacher-centred teaching methods and studentsâ€™ lack of enthusiasm. This study adopts
genetic algorithms, particle swarm algorithms, hybrid frog-leaping methods, and swarm intelligence
optimization algorithms to improve this situation. Through practical application, it is found that these methods
outperform traditional manual and composite methods. However, in-depth research reveals that swarm
intelligence algorithms have lower solving efficiency. To better meet practical application needs, a series of
convergence and precision adjustments are applied to the results obtained by the swarm intelligence algorithms.
Furthermore, improvements are made to the hybrid frog-leaping algorithm to cater to various engineering
improvement requirements. Using artificial intelligence algorithms to guide classroom activities expands
studentsâ€™ thinking and inspires their minds to better cope with multiple complex engineering challenges.