基于捕食-被捕食粒子群优化的模糊聚类  

Fuzzy clustering based on predator prey particle swarm optimization

作　　者：王琳[1] 罗可[1] 罗永红[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410076

出　　处：《计算机工程与应用》2008年第35期129-131,共3页Computer Engineering and Applications

基　　金：国家自然科学基金No.10871031;No.60474070;湖南省科技计划项目 (No.2008FJ3015);湖南省教育厅重点项目(No.07A001)~~

摘　　要：粒子群优化聚类算法具有参数简单,收敛快等优势,但也有局部极值问题。为解决此问题,提出一种基于捕食-被捕食的粒子群优化模糊聚类算法且聚类中心采用密度函数初始化。捕食者追逐被捕食者中心,加速收敛,而被捕食者逃离捕食者,促进多样性,以防局部极值出现。实验测试数据表明,算法具有防止局部极值、收敛快、全局寻优能力强等性能优势,能够比较好客观地反映现实世界。PSO clustering algorithm is known to have simple parameters and fast convergence,but there are also local optimal problems.To solve the problem, a fuzzy clustering based on predator prey PSO algorithm is presented,which is using density function to initialize cluster centre.Predators chase preys centre,to accelerate convergence,and the prey escape predators,to promote diversity and to prevent the local optimal there.The experimental test data show that this method is limited to prevent the extreme,fast convergence, global optimization capabilities,and other performance advantages ,better able to objectively reflect the real world.

关 键 词：捕食-被捕食 粒子群优化 模糊聚类 密度函数 局部极值 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]