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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.
This paper proposes an improved Particle Swarm Optimization (PSO) algorithm to solve the learning path optimization problem in online learning system, which takes into account factors such as learner’s learning objectives, knowledge mastery level, learning cost and resource relevance, Neighborhood search and tabu search to improve the performance of standard particle swarm optimization.The experimental results show that this method is highly practical and accurate and is an effective solution to learning path optimization problem.