灰 狼 优 化 算 法 ( G W O ) 原 理

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) Convergence_curve(l)=Alpha_score; 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 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); %% 显示程序运行时间

% 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; % If each variable has a different lb and ub if Boundary_no1 for i=1:dim ub_i=ub(i); lb_i=lb(i);

Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i)+lb_i; beta_max=para(4); % 缩放因子上界 Upper Bound of Scaling Factor

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

探狼：将解空间中除了头狼之外的最佳的S_num头人工狼，在猎物的活动范围内游动，根据空气中猎物留下的气味进行自主决策，气味越浓表明狼距离猎物越近，探狼始终朝着气味最浓的方向搜寻 test_wine

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

semilogy(GWO_cg_curve,'Color','r') %y轴以10为底的对数形式与x对应

钟一文.?智能优化方法及其应用研究[D].浙江大学,2005. Positions(i,j)=(X1+X2+X3)-3;% Equation (3.7)

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

=

（1）归一化：The fuzzy method has been implemented to normalize the objective functions which are not in the same range.

v[\+ str(l+1)] = beta * v[\+ str(l + 1)] + (1 - beta) * grads[\