基于粒子群的聚类算法 matlab,Matlab关于粒子群聚类算法

%2009-7-8

%粒子群聚类算法

function [m_pattern]=C_PSO(m_pattern,patternNum)

disType=DisSelDlg();%获得距离计算型

[centerNum iterNum]=InputClassDlg;%获得类中心数和最大迭代次数

particleNum=200;%初始化粒子数

%初始化中心和速度

global Nwidth;

for i=1:centerNum

m_center(i).feature=zeros(Nwidth,Nwidth);

m_center(i).patternNum=0;

m_center(i).index=i;

m_velocity(i).feature=zeros(Nwidth, Nwidth);

for i=1:particleNum

Particle(i).location=m_center;%粒子各中心

Particle(i).velocity=m_velocity;%粒子各中心速度

Particle(i).fitness=0;%适应度

P_id(i).velocity=m_velocity;%粒子最优速度

P_id(i).location=m_center;%粒子最优中心

P_id(i).fitness=0;%粒子最优适应度

end

P_gd.location=m_center;%全局粒子最优中心

P_gd.velocity=m_velocity;%全局粒子最优速度

P_gd.fitness=0;%粒子全局最优适应度

P_gd.string=zeros(1,patternNum);

ptDitrib=zeros(particleNum,patternNum);%初始化粒子分布矩阵

for i=1:particleNum %生产随机粒子分布矩阵

ptDitrib(i,:)=randperm(patternNum);

for j=1:patternNum

if (ptDitrib(i,j)>centerNum)

ptDitrib(i,j)=fix(rand*centerNum+1);

end

end

end

%生产初始粒子群

for i=1:particleNum

for j=1:patternNum

m_pattern(j).category=ptDitrib(i,j);

end

for j=1:centerNum

m_center(j)=CalCenter(m_center(j),m_pattern,patternNum);

end

Particle(i).location=m_center;

end

%初始化参数

w_max=1;

w_min=0;

h1=2;

h2=2;

for iter=1:iterNum

%计算粒子适应度

for i=1:particleNum

temp=0;

for j=1:patternNum

temp=temp+GetDistance(m_pattern(j),Particle(i).location(ptDitrib(i,j)),disType);

end

if (temp==0)%最优解，直接退出

iter=iterNum+1;

break;

end

Particle(i).fitness=1/temp;

end

if(iter>iterNum)

break;

end

w=w_max-iter*(w_max-w_min)/iterNum;%更新权重系数

for i=1:particleNum %更新P_id,P_gd

if (Particle(i).fitness>P_id(i).fitness)

P_id(i).fitness=Particle(i).fitness;

P_id(i).location=Particle(i).location;

P_id(i).velocity=Particle(i).velocity;

if (Particle(i).fitness>P_gd(i).fitness)

P_gd(i).fitness=Particle(i).fitness;

P_gd(i).location=Particle(i).location;

P_gd(i).velocity=Particle(i).velocity;

P_gd.string=ptDitrib(i,:);

end

end

end

%更新粒子速度，位置

for i=1:particleNum

for j=1:centerNum

Particle(i).velocity(j).feature =...

w*Particle(i).velocity(j).feature+h1*rand(Nwidth,Nwidth).*...

(P_id(i).location(j).feature-Particle(i).location(j).feature)...

+h2*rand(Nwidth, Nwidth).*(P_gd.location(j).feature...

-Particle(i).location(j).feature);

Particle(i).location(j).feature= Particle(i).location(j).feature...

+ Particle(i).velocity(j).feature;

end

end

%最近邻聚类

for i=1:particleNum

for j=1:patternNum

min=inf;

for k=1:centerNum

tempDis=GetDistance(m_pattern(j), Particle(i).location(k),disType);

if(tempDis

min=tempDis;

m_pattern(j).category=k;

ptDitrib(i,j)=k;

end

end

end

%重新计算聚类中心

for j=1:centerNum

Particle(i).location(j)=CalCenter(Particle(i).location(j),m_pattern,patternNum);

end

end

for i=1:patternNum

m_pattern(i).category=P_gd.string(1,i);

end

end

end