基于蚁群-粒子群混合算法的学习路径推荐策略研究

Learning Path Recommendation Strategy Based on Ant Colony and Particle Swarm Optimization Algorithm

针对目前学习路径推荐方法存在学习路径匹配度不高的问题,建立学习者和学习对象模型,综合考虑学习者的认知水平、学习风格与学习对象的难度、类型、目标知识点关联度的匹配情况等因素,使用粒子群算法搜索到次优路径后,再使用蚁群算法搜索最短路径,有效解决了单一的蚁群算法初期搜索方向盲目性的缺点。仿真结果表明,算法的求解速度和寻优性能得到了有效提高。

The current method of learning path recommendationhas the problem that the learning path matching degree is not high enough.In this paper,alearner and the learning object model isestablished.The model deals with the factors like the cognitive level and the learning style of the learner,the difficulty and resource typeof the learning object,and the relevance degree of the target knowledge point,etc.After that,the particle swarm optimization algorithm is used to search for the suboptimal path,and then the ant colony algorithm is used to search for the shortest path.These techniqueseffectively solve the shortcoming of the blindness of the initial search direction of the single ant colony algorithm.The simulation results show that the convergence speed and optimization performance of the algorithm are effectively improved.