关键词: 基因表达谱, 粒子群优化, 粒子对, 聚类分析

Abstract: Clustering techniques can extract biomedical information from gene expression data.The basic particle swarm optimization is liable to be trapped in local convergence and sensitive to adjustable parameters.A new algorithm is proposed to solve gene clustering problem.Four particles are initialized and two of them are chosen randomly as a pair.The result of K-means is assigned to one particle in the pair.At each iteration，every particle updates itself only according to its velocity and the best position of the pair it belongs to.One elitist particle is generated from each pair respectively and they are combined into a new pair.The new particle pair continues to search and its best position is just the global optima of the algorithm.Experiment results show that the algorithm has perfect homogeneity and separation in yeast gene expression data and has an advantage in computation time and convergence rate.

Key words: gene expression profile, particle swarm optimization, particle pair, clustering analysis