提出一种改进粒子群算法求解在线学习系统中的学习路径优化问题. 在建模时综合考虑了学习者的学习目标、知识掌握水平、学习成本和资源相关度等因素; 在寻优时采用局部邻域搜索与禁忌搜索相结合的方式, 以改进标准粒子群方法的寻优性能. 实验结果表明, 该方法具有较高的实用性和准确性, 是学习路径优化问题的一种有效求解算法.

An improved particle swarm optimization (PSO) algorithm was presented to resolve the learning path optimization problem in online learning system. Studying objectives of learners, level of mastering knowledge, studying cost and relevance of resources have been taken into account during constructing the mathematical model. And the model combines local neighborhood search method with Tabu Search for enhancing PSO's optimal searching performance. The experimental result shows that the algorithm has high practicability and accuracy, which proves to be an efficient solving algorithm for the learning path optimization problem.