基于改进粒子群算法的数据预测挖掘应用研究

Application Research on Improved PSO Algorithm for Data Prediction Mining

针对粒子群优化算法早熟、易陷入局部收敛的问题,提出一种克服早熟的粒子群算法.该算法在标准粒子群算法基础上加入极值扰动和自适应调整系数,使其易于跳出局部最优。又分析了灰色GM（1,1）预测模型的局限性,提出了一种带极值扰动的自适应调整惯性权重的改进PSO优化灰色模型AdPSO-GM,并将此模型用于数据预测挖掘研究中。最后,通过一个实例对所提方法进行验证,结果表明,本文所给模型具有较高的预测挖掘精度。

In order to solve the problem of prematurity and tendancy to fall into local convergence in particle swarm optimization algorithm,this paper proposed an improved particle swarm optimization algorithm that is able to overcome prematurity.Extreme disturbances and adaptive adjustment factor were added to the standard PSO algorithm.Making the algorithm can jump out of local optimum easily.It also analyzed the limitations of gray model GM（1,1 ）.So a selfadaptive PSO algorithm with disturbed extremum called AdPSO is presented.Utilizing the new model for data mining prediction. Finally,an example is used to validate the proposed method.Example shows that this model has higher prediction accuracy.