粒子群优化算法在群体学习行为设计中的应用研究

Research on particle swarm optimization applied in group learning behavior design

作为群智能方法之一,粒子群算法具有重要学术意义和现实价值.群体学习行为的优化设计是教育领域目前比较关注的问题.利用粒子群算法以追求最优教育目标为目的,通过模拟实际教学动态过程,设计最优学习路线,用于求解如何获得最佳学习效果问题.实验表明粒子群优化算法在求解高校群体学习行为优化问题比较有效,优化结果比较理想,大学生的群体学习行为得到了较好的优化.实践证明:粒子群优化算法具有简单容易实现的特点,较好地改善了学习效果,能在花费较少学习时间和精力的情况下获得较好学习效果.

As the method of group intelligent methods, Particle Swarm Optimization （PSO） has important academic significance and practical value. The optimal design of group learning behavior is the issue which had been concerned in the field of education. By utilizing PSO for the purpose which pursuing the best education goals, the dynamic process was simulated that of the actual teaching, optimal learning routes were designed for solving the problem how to obtain the best learning results. Experimental results show that PSO in solving problems of university group learning behavior optimization is more effectively , and optimized result is relatively ideal , university students group learning behavior are optimized, PSO has simple and easy to implement features which could improve the learning effect in a better way and receive better learning case on spending minimum learning time and effort.