粒子群优化的支持向量机matlab源代码

%% 清空环境 clc clear load matlab111.mat %% 载入数据 train= data_train'; train_label =label_train'; test =data_test'; test_label =label_test'; [train,pstrain] = mapminmax(train'); pstrain.ymin = 0; pstrain.ymax = 1; [train,pstrain] = mapminmax(train,pstrain); [test,pstest] = mapminmax(test'); pstest.ymin = 0; pstest.ymax = 1; [test,pstest] = mapminmax(test,pstest); train = train'; test = test'; %% 参数初始化 %粒子群算法中的两个参数 c1 = 1.6; % c1 belongs to [0,2] c2 = 1.5; % c2 belongs to [0,2] maxgen=300; % 进化次数 sizepop=30; % 种群规模 popcmax=10^(2); popcmin=10^(-1); popgmax=10^(3); popgmin=10^(-2); k = 0.6; % k belongs to [0.1,1.0]; Vcmax = k*popcmax; Vcmin = -Vcmax ; Vgmax = k*popgmax; Vgmin = -Vgmax ; % SVM参数初始化 v = 3; %% 产生初始粒子和速度 for i=1:sizepop % 随机产生种群 pop(i,1) = (popcmax-popcmin)*rand+popcmin; % 初始种群 pop(i,2) = (popgmax-popgmin)*rand+popgmin; V(i,1)=Vcmax*rands(1); % 初始化速度 V(i,2)=Vgmax*rands(1); % 计算初始适应度 cmd = ['-v ',num2str(v),' -c ',num2str( pop(i,1) ),' -g ',num2str( pop(i,2) )]; fitness(i) = svmtrain(train_label, train, cmd); fitness(i) = -fitness(i); end % 找极值和极值点 [global_fitness bestindex]=min(fitness); % 全局极值 local_fitness=fitness; % 个体极值初始化 global_x=pop(bestindex,:); % 全局极值点 local_x=pop; % 个体极值点初始化 tic %% 迭代寻优 for i=1:maxgen for j=1:sizepop %速度更新 wV = 0.9; % wV best belongs to [0.8,1.2] V(j,:) = wV*V(j,:) + c1*rand*(local_x(j,:) - pop(j,:)) + c2*rand*(global_x - pop(j,:)); if V(j,1) > Vcmax V(j,1) = Vcmax; end if V(j,1) < Vcmin V(j,1) = Vcmin; end if V(j,2) > Vgmax V(j,2) = Vgmax; end if V(j,2) < Vgmin V(j,2) = Vgmin; end %种群更新 wP = 0.6; pop(j,:)=pop(j,:)+wP*V(j,:); if pop(j,1) > popcmax pop(j,1) = popcmax; end if pop(j,1) < popcmin pop(j,1) = popcmin; end if pop(j,2) > popgmax pop(j,2) = popgmax; end if pop(j,2) < popgmin pop(j,2) = popgmin; end % 自适应粒子变异 if rand>0.5 k=ceil(2*rand); if k == 1 pop(j,k) = (20-1)*rand+1; end if k == 2 pop(j,k) = (popgmax-popgmin)*rand+popgmin; end end %适应度值 cmd = ['-v ',num2str(v),' -c ',num2str( pop(j,1) ),' -g ',num2str( pop(j,2) )]; fitness(j) = svmtrain(train_label, train, cmd); fitness(j) = -fitness(j); end %个体最优更新 if fitness(j) < local_fitness(j) local_x(j,:) = pop(j,:); local_fitness(j) = fitness(j); end %群体最优更新 if fitness(j) < global_fitness global_x = pop(j,:); global_fitness = fitness(j); end fit_gen(i)=global_fitness; end toc %% 结果分析 plot(-fit_gen,'LineWidth',5); title(['适应度曲线','(参数c1=',num2str(c1),',c2=',num2str(c2),',终止代数=',num2str(maxgen),')'],'FontSize',13); xlabel('进化代数');ylabel('适应度'); bestc = global_x(1) bestg = global_x(2) bestCVaccuarcy = -fit_gen(maxgen) cmd = ['-c ',num2str( bestc ),' -g ',num2str( bestg )]; model = svmtrain(train_label,train,cmd); [trainpre,trainacc] = svmpredict(train_label,train,model); trainacc [testpre,testacc] = svmpredict(test_label,test,model); testacc figure; hold on; plot(test_label,'o'); plot(testpre,'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;