以优化SVM算法的参数c和g为例，对GWO算法MATLAB源码进行了逐行中文注解。

tic % 计时器

%% 清空环境变量

close all

clear

clc

format compact

%% 数据提取

% 载入测试数据wine,其中包含的数据为classnumber = 3,wine:178*13的矩阵,wine_labes:178*1的列向量

load wine.mat

% 选定训练集和测试集

% 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集

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

% 相应的训练集的标签也要分离出来

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

% 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集

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

% 相应的测试集的标签也要分离出来

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

%% 数据预处理

% 数据预处理,将训练集和测试集归一化到[0,1]区间

[mtrain,ntrain] = size(train_wine);

[mtest,ntest] = size(test_wine);

dataset = [train_wine;test_wine];

% mapminmax为MATLAB自带的归一化函数

[dataset_scale,ps] = mapminmax(dataset',0,1);

dataset_scale = dataset_scale';

train_wine = dataset_scale(1:mtrain,:);

test_wine = dataset_scale( (mtrain+1):(mtrain+mtest),: );

%% 利用灰狼算法选择最佳的SVM参数c和g

SearchAgents_no=10; % 狼群数量，Number of search agents

Max_iteration=10; % 最大迭代次数，Maximum numbef of iterations

dim=2; % 此例需要优化两个参数c和g，number of your variables

lb=[0.01,0.01]; % 参数取值下界

ub=[100,100]; % 参数取值上界

% v = 5; % SVM Cross Validation参数,默认为5

% initialize alpha, beta, and delta_pos

Alpha_pos=zeros(1,dim); % 初始化Alpha狼的位置

Alpha_score=inf; % 初始化Alpha狼的目标函数值，change this to -inf for maximization problems

Beta_pos=zeros(1,dim); % 初始化Beta狼的位置

Beta_score=inf; % 初始化Beta狼的目标函数值，change this to -inf for maximization problems

Delta_pos=zeros(1,dim); % 初始化Delta狼的位置

Delta_score=inf; % 初始化Delta狼的目标函数值，change this to -inf for maximization problems

%Initialize the positions of search agents

Positions=initialization(SearchAgents_no,dim,ub,lb);

Convergence_curve=zeros(1,Max_iteration);

l=0; % Loop counter循环计数器

% Main loop主循环

while l

for i=1:size(Positions,1) % 遍历每个狼

% Return back the search agents that go beyond the boundaries of the search space

% 若搜索位置超过了搜索空间，需要重新回到搜索空间

Flag4ub=Positions(i,:)>ub;

Flag4lb=Positions(i,:)

% 若狼的位置在最大值和最小值之间，则位置不需要调整，若超出最大值，最回到最大值边界；

% 若超出最小值，最回答最小值边界

Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; % ~表示取反

% 计算适应度函数值

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

model=svmtrain(train_wine_labels,train_wine,cmd); % SVM模型训练

[~,fitness]=svmpredict(test_wine_labels,test_wine,model); % SVM模型预测及其精度

fitness=100-fitness(1); % 以错误率最小化为目标

% Update Alpha, Beta, and Delta

if fitness

Alpha_score=fitness; % 则将Alpha狼的目标函数值更新为最优目标函数值，Update alpha

Alpha_pos=Positions(i,:); % 同时将Alpha狼的位置更新为最优位置

end

if fitness>Alpha_score && fitness

Beta_score=fitness; % 则将Beta狼的目标函数值更新为最优目标函数值，Update beta

Beta_pos=Positions(i,:); % 同时更新Beta狼的位置

end

if fitness>Alpha_score && fitness>Beta_score && fitness

Delta_score=fitness; % 则将Delta狼的目标函数值更新为最优目标函数值，Update delta

Delta_pos=Positions(i,:); % 同时更新Delta狼的位置

end

end

a=2-l*((2)/Max_iteration); % 对每一次迭代，计算相应的a值，a decreases linearly fron 2 to 0

% Update the Position of search agents including omegas

for i=1:size(Positions,1) % 遍历每个狼

for j=1:size(Positions,2) % 遍历每个维度

% 包围猎物，位置更新

r1=rand(); % r1 is a random number in [0,1]

r2=rand(); % r2 is a random number in [0,1]

A1=2*a*r1-a; % 计算系数A，Equation (3.3)

C1=2*r2; % 计算系数C，Equation (3.4)

% Alpha狼位置更新

D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1

X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1

r1=rand();

r2=rand();

A2=2*a*r1-a; % 计算系数A，Equation (3.3)

C2=2*r2; % 计算系数C，Equation (3.4)

% Beta狼位置更新

D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2

X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2

r1=rand();

r2=rand();

A3=2*a*r1-a; % 计算系数A，Equation (3.3)

C3=2*r2; % 计算系数C，Equation (3.4)

% Delta狼位置更新

D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3

X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3

% 位置更新

Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)

end

end

l=l+1;

Convergence_curve(l)=Alpha_score;

end

bestc=Alpha_pos(1,1);

bestg=Alpha_pos(1,2);

bestGWOaccuarcy=Alpha_score;

%% 打印参数选择结果

disp('打印选择结果');

str=sprintf('Best Cross Validation Accuracy = %g%%，Best c = %g，Best g = %g',bestGWOaccuarcy*100,bestc,bestg);

disp(str)

%% 利用最佳的参数进行SVM网络训练

cmd_gwosvm = ['-c ',num2str(bestc),' -g ',num2str(bestg)];

model_gwosvm = svmtrain(train_wine_labels,train_wine,cmd_gwosvm);

%% SVM网络预测

[predict_label,accuracy] = svmpredict(test_wine_labels,test_wine,model_gwosvm);

% 打印测试集分类准确率

total = length(test_wine_labels);

right = sum(predict_label == test_wine_labels);

disp('打印测试集分类准确率');

str = sprintf( 'Accuracy = %g%% (%d/%d)',accuracy(1),right,total);

disp(str);

%% 结果分析

% 测试集的实际分类和预测分类图

figure;

hold on;

plot(test_wine_labels,'o');

plot(predict_label,'r*');

xlabel('测试集样本','FontSize',12);

ylabel('类别标签','FontSize',12);

legend('实际测试集分类','预测测试集分类');

title('测试集的实际分类和预测分类图','FontSize',12);

grid on

snapnow

%% 显示程序运行时间

toc

% This function initialize the first population of search agents

function Positions=initialization(SearchAgents_no,dim,ub,lb)

Boundary_no= size(ub,2); % numnber of boundaries

% If the boundaries of all variables are equal and user enter a signle

% number for both ub and lb

if Boundary_no==1

Positions=rand(SearchAgents_no,dim).*(ub-lb)+lb;

end

% If each variable has a different lb and ub

if Boundary_no>1

for i=1:dim

ub_i=ub(i);

lb_i=lb(i);

Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i)+lb_i;

end

end

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作者：Genlovy_Hoo

来源：CSDN

原文：https://blog.csdn.net/u013337691/article/details/52468552

版权声明：本文为博主原创文章，转载请附上博文链接！