量子行为粒子群算法在基因聚类中的应用

在PSO算法的基础上提出的基于量子行为的QPSO算法，并将其应用到基因表达数据集上。QPSO基因聚类算法是将N条基因根据使TWCV（Total Within-Cluster Variation）函数值达到最小分到由用户指定的K个聚类中。根据K-means算法的优点，利用K-means聚类的结果重新初始化粒子群，结合QPSO和PSO的聚类算法提出了KQPSO和KPSO算法。通过在4个实验数据集上利用K-means、PSO、QPSO、KPSO、KQPSO 5个聚类算法得出的结果比较显示QPSO算法在基因表达数据分析上具有良好的性能。

Application for gene clustering based on quantum-behaved particle swarm algorithm

It proposes quantum-behaved particle swarm optimization QPSO algorithm on the basis of the PSO algorithm and applies it to a data set on gene expression.The proposed clustering algorithm partitions the N patterns of the gene expression dataset into user-defined K categories to minimize the fitness function of total within-cluster variation.Based on the merits of K-means algorithm and using K-means clustering to seed the initial swarm,combing QPSO,PSO to cluster data,it proposes KQPSO,KPSO algorithm.The experiment results on four gene expression data sets using K-means,PSO,QPSO,KPSO,KQPSO five clustering algorithms show that the QPSO-based clustering algorithm has a good performance in gene expression data analysis.