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 1X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1r1=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 2X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2r1=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 3X3=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

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