利用PSO优化SVM

%% 清空环境

clc

clear

load wine;

train = [wine(1:30,:);wine(60:95,:);wine(131:153,:)];

train_label = [wine_labels(1:30);wine_labels(60:95);wine_labels(131:153)];

test = [wine(31:59,:);wine(96:130,:);wine(154:178,:)];

test_label = [wine_labels(31:59);wine_labels(96:130);wine_labels(154:178)];

[train,pstrain] = mapminmax(train');

pstrain.ymin = 0;

pstrain.ymax = 1;

[train,pstrain] = mapminmax(train,pstrain);

[test,pstest] = mapminmax(test');

pstest.ymin = 0;

pstest.ymax = 1;

[test,pstest] = mapminmax(test,pstest);

train = train';

test = test';

%% 参数初始化

%粒子群算法中的两个参数

c1 = 1.6; % c1 belongs to [0,2]

c2 = 1.5; % c2 belongs to [0,2]

maxgen=300; % 进化次数

sizepop=30; % 种群规模

popcmax=10^(2);

popcmin=10^(-1);

popgmax=10^(3);

popgmin=10^(-2);

k = 0.6; % k belongs to [0.1,1.0];

Vcmax = k*popcmax;

Vcmin = -Vcmax ;

Vgmax = k*popgmax;

Vgmin = -Vgmax ;

% SVM参数初始化

v = 3;

%% 产生初始粒子和速度

for i=1:sizepop

% 随机产生种群

pop(i,1) = (popcmax-popcmin)*rand+popcmin; % 初始种群

pop(i,2) = (popgmax-popgmin)*rand+popgmin;

V(i,1)=Vcmax*rands(1); % 初始化速度

V(i,2)=Vgmax*rands(1);

% 计算初始适应度

cmd = ['-v ',num2str(v),' -c ',num2str( pop(i,1) ),' -g ',num2str( pop(i,2) )];

fitness(i) = svm train(train_label, train, cmd);

fitness(i) = -fitness(i);

end

% 找极值和极值点

[global_fitness bestindex]=min(fitness); % 全局极值

local_fitness=fitness; % 个体极值初始化

global_x=pop(bestindex,:); % 全局极值点

local_x=pop; % 个体极值点初始化

tic

%% 迭代寻优

for i=1:maxgen

for j=1:sizepop

%速度更新

wV = 0.9; % wV best belongs to [0.8,1.2]

V(j,:) = wV*V(j,:) + c1*rand*(local_x(j,:) - pop(j,:)) + c2*rand*(global_x - pop(j,:));

if V(j,1) > Vcmax

V(j,1) = Vcmax;

end

if V(j,1) < Vcmin

V(j,1) = Vcmin;

end

if V(j,2) > Vgmax

V(j,2) = Vgmax;

end

if V(j,2) < Vgmin

V(j,2) = Vgmin;

end

%种群更新

wP = 0.6;

pop(j,:)=pop(j,:)+wP*V(j,:);

if pop(j,1) > popcmax

pop(j,1) = popcmax;

end

if pop(j,1) < popcmin

pop(j,1) = popcmin;

end

if pop(j,2) > popgmax

pop(j,2) = popgmax;

end

if pop(j,2) < popgmin

pop(j,2) = popgmin;

end

% 自适应粒子变异

if rand>0.5

k=ceil(2*rand);

if k == 1

pop(j,k) = (20-1)*rand+1;

end

if k == 2

pop(j,k) = (popgmax-popgmin)*rand+popgmin;

end

end

%适应度值

cmd = ['-v ',num2str(v),' -c ',num2str( pop(j,1) ),' -g ',num2str( pop(j,2) )];

fitness(j) = svmtrain(train_label, train, cmd);

fitness(j) = -fitness(j);

end

%个体最优更新

if fitness(j) < local_fitness(j)

local_x(j,:) = pop(j,:);

local_fitness(j) = fitness(j);

end