灰狼优化算法源代码.rar

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<Max_iteration % 对迭代次数循环 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,:)<lb; % 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=fitcsvm(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 % 如果目标函数值小于Alpha狼的目标函数值 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 % 如果目标函数值介于于Alpha狼和Beta狼的目标函数值之间 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 % 如果目标函数值介于于Beta狼和Delta狼的目标函数值之间 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 = fitcsvm(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