灰狼优化算法——MATLAB

  1 tic % 计时器
  2 %% 清空环境变量
  3 close all
  4 clear
  5 clc
  6 format compact
  7 %% 数据提取
  8 % 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
  9 load wine.mat
 10 % 选定训练集和测试集
 11 % 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集
 12 train_wine = [wine(1:30,:);wine(60:95,:);wine(131:153,:)];
 13 % 相应的训练集的标签也要分离出来
 14 train_wine_labels = [wine_labels(1:30);wine_labels(60:95);wine_labels(131:153)];
 15 % 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集
 16 test_wine = [wine(31:59,:);wine(96:130,:);wine(154:178,:)];
 17 % 相应的测试集的标签也要分离出来
 18 test_wine_labels = [wine_labels(31:59);wine_labels(96:130);wine_labels(154:178)];
 19 %% 数据预处理
 20 % 数据预处理,将训练集和测试集归一化到[0,1]区间
 21 [mtrain,ntrain] = size(train_wine);
 22 [mtest,ntest] = size(test_wine);
 23 
 24 dataset = [train_wine;test_wine];
 25 % mapminmax为MATLAB自带的归一化函数
 26 [dataset_scale,ps] = mapminmax(dataset',0,1);
 27 dataset_scale = dataset_scale';
 28 
 29 train_wine = dataset_scale(1:mtrain,:);
 30 test_wine = dataset_scale( (mtrain+1):(mtrain+mtest),: );
 31 %% 利用灰狼算法选择最佳的SVM参数c和g
 32 SearchAgents_no=10; % 狼群数量，Number of search agents
 33 Max_iteration=10; % 最大迭代次数，Maximum numbef of iterations
 34 dim=2; % 此例需要优化两个参数c和g，number of your variables
 35 lb=[0.01,0.01]; % 参数取值下界
 36 ub=[100,100]; % 参数取值上界
 37 % v = 5; % SVM Cross Validation参数,默认为5
 38 
 39 % initialize alpha, beta, and delta_pos
 40 Alpha_pos=zeros(1,dim); % 初始化Alpha狼的位置
 41 Alpha_score=inf; % 初始化Alpha狼的目标函数值，change this to -inf for maximization problems
 42 
 43 Beta_pos=zeros(1,dim); % 初始化Beta狼的位置
 44 Beta_score=inf; % 初始化Beta狼的目标函数值，change this to -inf for maximization problems
 45 
 46 Delta_pos=zeros(1,dim); % 初始化Delta狼的位置
 47 Delta_score=inf; % 初始化Delta狼的目标函数值，change this to -inf for maximization problems
 48 
 49 %Initialize the positions of search agents
 50 Positions=initialization(SearchAgents_no,dim,ub,lb);
 51 
 52 Convergence_curve=zeros(1,Max_iteration);
 53 
 54 l=0; % Loop counter循环计数器
 55 
 56 % Main loop主循环
 57 while l 对迭代次数循环
 58     for i=1:size(Positions,1)  % 遍历每个狼
 59         
 60        % Return back the search agents that go beyond the boundaries of the search space
 61        % 若搜索位置超过了搜索空间，需要重新回到搜索空间
 62         Flag4ub=Positions(i,:)>ub;
 63         Flag4lb=Positions(i,:)<lb;
 64         % 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；
 65         % 若超出最小值，最回答最小值边界
 66         Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; % ~表示取反           
 67      
 68       % 计算适应度函数值
 69        cmd = [' -c ',num2str(Positions(i,1)),' -g ',num2str(Positions(i,2))];
 70        model=svmtrain(train_wine_labels,train_wine,cmd); % SVM模型训练
 71        [~,fitness]=svmpredict(test_wine_labels,test_wine,model); % SVM模型预测及其精度
 72        fitness=100-fitness(1); % 以错误率最小化为目标
 73     
 74         % Update Alpha, Beta, and Delta
 75         if fitness 如果目标函数值小于Alpha狼的目标函数值
 76             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
 77             Alpha_pos=Positions(i,:); % 同时将Alpha狼的位置更新为最优位置
 78         end
 79         
 80         if fitness>Alpha_score && fitness 如果目标函数值介于于Alpha狼和Beta狼的目标函数值之间
 81             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
 82             Beta_pos=Positions(i,:); % 同时更新Beta狼的位置
 83         end
 84         
 85         if fitness>Alpha_score && fitness>Beta_score && fitness 如果目标函数值介于于Beta狼和Delta狼的目标函数值之间
 86             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
 87             Delta_pos=Positions(i,:); % 同时更新Delta狼的位置
 88         end
 89     end
 90     
 91     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
 92     
 93     % Update the Position of search agents including omegas
 94     for i=1:size(Positions,1) % 遍历每个狼
 95         for j=1:size(Positions,2) % 遍历每个维度
 96             
 97             % 包围猎物，位置更新
 98             
 99             r1=rand(); % r1 is a random number in [0,1]
100             r2=rand(); % r2 is a random number in [0,1]
101             
102             A1=2*a*r1-a; % 计算系数A，Equation (3.3)
103             C1=2*r2; % 计算系数C，Equation (3.4)
104             
105             % Alpha狼位置更新
106             D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1
107             X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1
108                        
109             r1=rand();
110             r2=rand();
111             
112             A2=2*a*r1-a; % 计算系数A，Equation (3.3)
113             C2=2*r2; % 计算系数C，Equation (3.4)
114             
115             % Beta狼位置更新
116             D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2
117             X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2       
118             
119             r1=rand();
120             r2=rand(); 
121             
122             A3=2*a*r1-a; % 计算系数A，Equation (3.3)
123             C3=2*r2; % 计算系数C，Equation (3.4)
124             
125             % Delta狼位置更新
126             D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3
127             X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3             
128             
129             % 位置更新
130             Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)
131             
132         end
133     end
134     l=l+1;    
135     Convergence_curve(l)=Alpha_score;
136 end
137 bestc=Alpha_pos(1,1);
138 bestg=Alpha_pos(1,2);
139 bestGWOaccuarcy=Alpha_score;
140 %% 打印参数选择结果
141 disp('打印选择结果');
142 str=sprintf('Best Cross Validation Accuracy = %g%%，Best c = %g，Best g = %g',bestGWOaccuarcy*100,bestc,bestg);
143 disp(str)
144 %% 利用最佳的参数进行SVM网络训练
145 cmd_gwosvm = ['-c ',num2str(bestc),' -g ',num2str(bestg)];
146 model_gwosvm = svmtrain(train_wine_labels,train_wine,cmd_gwosvm);
147 %% SVM网络预测
148 [predict_label,accuracy] = svmpredict(test_wine_labels,test_wine,model_gwosvm);
149 % 打印测试集分类准确率
150 total = length(test_wine_labels);
151 right = sum(predict_label == test_wine_labels);
152 disp('打印测试集分类准确率');
153 str = sprintf( 'Accuracy = %g%% (%d/%d)',accuracy(1),right,total);
154 disp(str);
155 %% 结果分析
156 % 测试集的实际分类和预测分类图
157 figure;
158 hold on;
159 plot(test_wine_labels,'o');
160 plot(predict_label,'r*');
161 xlabel('测试集样本','FontSize',12);
162 ylabel('类别标签','FontSize',12);
163 legend('实际测试集分类','预测测试集分类');
164 title('测试集的实际分类和预测分类图','FontSize',12);
165 grid on
166 snapnow
167 %% 显示程序运行时间
168 toc

 

转载于:https://www.cnblogs.com/Wang-Wenhui/p/8882986.html