基于粒子群优化SVM的空气质量等级分类预测（代码完整，数据齐全）

function [bestCVmse,bestc,bestg,fit_gen] = psoSVMcgForRegress(train_label,train,T_test,P_test,pso_option) %% 参数初始化 % c1:pso参数局部搜索能力 % c2:pso参数全局搜索能力 % maxgen:最大进化数量 % sizepop:种群最大数量 % k:k belongs to [0.1,1.0],速率和x的关系(V = kX) % wV:(wV best belongs to [0.8,1.2]),速率更新公式中速度前面的弹性系数 % wP:种群更新公式中速度前面的弹性系数 % popcmax:SVM 参数c的变化的最大值. % popcmin:SVM 参数c的变化的最小值. % popgmax:SVM 参数g的变化的最大值. % popgmin:SVM 参数c的变化的最小值. % popkernel:SVM的核参数 Vcmax = pso_option.k*pso_option.popcmax; Vcmin = -Vcmax ; Vgmax = pso_option.k*pso_option.popgmax; Vgmin = -Vgmax ; %% 产生初始粒子和速度 for i=1:pso_option.sizepop % 随机产生种群和速度 i pop(i,1) = (pso_option.popcmax-pso_option.popcmin)*rand+pso_option.popcmin; pop(i,2) = (pso_option.popgmax-pso_option.popgmin)*rand+pso_option.popgmin; V(i,1)=Vcmax*rands(1,1); V(i,2)=Vgmax*rands(1,1); % 计算初始适应度 cmd = ['-s 3 -t ',num2str( pso_option.popkernel ),' -c ',num2str( pop(i,1) ),' -g ',num2str( pop(i,2) ),' -p 0.01 -d 1']; model= svmtrain(train_label, train, cmd); [l,~]= svmpredict(T_test,P_test,model); fitness(i)=mse(l-T_test);%以均方差作为适应度函数，均方差越小，精度越高 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 % 每一代种群的平均适应度 avgfitness_gen = zeros(1,pso_option.maxgen); %% 迭代寻优 for i=1:pso_option.maxgen iter=i for j=1:pso_option.sizepop %速度更新 V(j,:) = pso_option.wV*V(j,:) + pso_option.c1*rand*(local_x(j,:) - pop(j,:)) + pso_option.c2*rand*(global_x - pop(j,:)); % 边界判断 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 pop(j,:)=pop(j,:) + pso_option.wP*V(j,:); %边界判断 if pop(j,1) > pso_option.popcmax pop(j,1) = (pso_option.popcmax-pso_option.popcmin)*rand+pso_option.popcmin;; end if pop(j,1) < pso_option.popcmin pop(j,1) = (pso_option.popcmax-pso_option.popcmin)*rand+pso_option.popcmin;; end if pop(j,2) > pso_option.popgmax pop(j,2) = (pso_option.popgmax-pso_option.popgmin)*rand+pso_option.popgmin;; end if pop(j,2) < pso_option.popgmin pop(j,2) = (pso_option.popgmax-pso_option.popgmin)*rand+pso_option.popgmin;; end % 自适应粒子变异 if rand>0.8 k=ceil(2*rand); if k == 1 pop(j,k) = (pso_option.popcmax-pso_option.popcmin)*rand + pso_option.popcmin; end if k == 2 pop(j,k) = (pso_option.popgmax-pso_option.popgmin)*rand + pso_option.popgmin; end end %适应度值 cmd = ['-t ',num2str( pso_option.popkernel ),' -c ',num2str( pop(j,1) ),' -g ',num2str( pop(j,2) ),' -s 3 -p 0.01 -d 1']; model= svmtrain(train_label, train, cmd); [l,mse1]= svmpredict(T_test,P_test,model); fitness(j)=mse(l-T_test); %个体最优更新 if fitness(j) < local_fitness(j) local_x(j,:) = pop(j,:); local_fitness(j) = fitness(j); end if fitness(j) == local_fitness(j) && pop(j,1) < local_x(j,1) 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 end fit_gen(i)=global_fitness; avgfitness_gen(i) = sum(fitness)/pso_option.sizepop; end %% 输出结果 % 最好的参数 bestc = global_x(1); bestg = global_x(2); bestCVmse = fit_gen(pso_option.maxgen);%最好的结果