Abstract: The immune information evolutionary mechanism of artificial immune system is used into Particle Swarm Optimization(PSO) algorithm, a new clustering algorithm based on C-means and improved PSO is presented, it can avoid “early ripe” of PSO and traditional clustering algorithm. New algorithm chooses the suitable inertia weight for every swarm through the linearly decreasing weight policy, and uses the immune evolutionary principle to improve the process of PSO. According to the experiential rule of classical clustering theory and swarm performance cost function, the new swarm is generated above the best particle and then find the best k. Simulation experiments show that this method outperforms the classical clustering algorithm in convergence ability and it has the advantages of high accuracy of clustering and good clustering ability.

Key words: immune evolutionary mechanism, Particle Swarm Optimization(PSO), linearly decreasing weight, dynamic clustering

摘要： 针对粒子群优化算法和传统聚类算法易产生“早熟”现象的不足，把人工免疫系统的免疫信息进化处理机制引入到粒子群优化算法，提出一种基于免疫进化粒子群的动态聚类算法。算法采用线性递减权策略为各个粒子选取适当惯性权值，利用免疫进化思想改进粒子群优化过程，同时利用聚类经验规则 确定聚类数k的初始搜索范围，以性能代价函数为依据在聚类数目未知的情况下实现动态聚类。仿真实验表明，新算法有效提高聚类正确率，具有收敛精度高和聚类能力强等特点。

关键词: 免疫进化机制, 粒子群优化, 线性递减权, 动态聚类

CLC Number: 

• TP18