PSO-lssvm.zip_-baijiahao_PSO—LSSVM_PSO优化 lssvm_pso优化LSSVM_最小二乘代码

function [bestCVaccuarcy,bestc,bestg,pso_option] = psoLSSVMcgForClass(trainset,trainset_label,Yc,pso_option) % psoLSSVMcgForClass %by Tangxiaobiao QQ 444646122 blog.sina.com.cn/lssvm % 2010.05.31 %% 参数初始化 if nargin == 3 pso_option = struct('c1',1.5,'c2',1.7,'maxgen',200,'sizepop',20, ... 'k',0.6,'wV',1,'wP',1,'v',5, ... 'popcmax',10^2,'popcmin',10^(-1),'popgmax',10^3,'popgmin',10^(-2)); end % c1:初始为1.5,pso参数局部搜索能力 % c2:初始为1.7,pso参数全局搜索能力 % maxgen:初始为200,最大进化数量 % sizepop:初始为20,种群最大数量 % k:初始为0.6(k belongs to [0.1,1.0]),速率和x的关系(V = kX) % wV:初始为1(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:初始为1,种群更新公式中速度前面的弹性系数 % v:初始为3,SVM Cross Validation参数 % popcmax:初始为100,SVM 参数c的变化的最大值. % popcmin:初始为0.1,SVM 参数c的变化的最小值. % popgmax:初始为1000,SVM 参数g的变化的最大值. % popgmin:初始为0.01,SVM 参数c的变化的最小值. Yc=Yc; Vcmax = pso_option.k*pso_option.popcmax; Vcmin = -Vcmax ; Vgmax = pso_option.k*pso_option.popgmax; Vgmin = -Vgmax ; eps = 10^(-3); [Yc,codebook,old_codebook] = code(trainset_label,'code_MOC'); %% 产生初始粒子和速度 for i=1:pso_option.sizepop % 随机产生种群和速度 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); V(i,2)=Vgmax*rands(1); % 计算初始适应度 gam=pop(i,1);sig2=pop(i,2); model=initlssvm(trainset,Yc,'c',gam,sig2,'RBF_kernel'); model=trainlssvm(model); Yd0=simlssvm(model,trainset); predict_label = code(Yd0,old_codebook,[],codebook);%解码分类结果 testnum=size(trainset_label,1); right=sum(trainset_label==predict_label); n = sum(sum(trainset_label~=predict_label)); fitness(i) = (1-n/prod(size(trainset_label)))*100; 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 % 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 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,:)); 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,:); if pop(j,1) > pso_option.popcmax pop(j,1) = pso_option.popcmax; end if pop(j,1) < pso_option.popcmin pop(j,1) = pso_option.popcmin; end if pop(j,2) > pso_option.popgmax pop(j,2) = pso_option.popgmax; end if pop(j,2) < pso_option.popgmin pop(j,2) = pso_option.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) = (pso_option.popgmax-pso_option.popgmin)*rand + pso_option.popgmin; end end %适应度值 gam=pop(j,1);sig2=pop(j,2); model=initlssvm(trainset,Yc,'c',gam,sig2,'RBF_kernel'); model=trainlssvm(model); Yd0=simlssvm(model,trainset); predict_label = code(Yd0,old_codebook,[],codebook);%解码分类结果 testnum=size(trainset_label,1); right=sum(trainset_label==predict_label); n = sum(sum(trainset_label~=predict_label)); fitness(j) = (1-n/prod(size(trainset_label)))*100; fitness(j) = -fitness(j); gam=pop(j,1);sig2=pop(j,2); model=initlssvm(trainset,Yc,'c',gam,sig2,'RBF_kernel'); model=trainlssvm(model); if fitness(j) >= -65 continue; end %个体最优更新 if fitness(j) < local_fitness(j) local_x(j,:) = pop(j,:); local_fitness(j) = fitness(j); end if abs( fitness(j)-local_fitness(j) )<=eps && 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 if abs( fitness(j)-global_fitness )<=eps && pop(j,1) < global_x(1) global_x = pop(j,:); global_fitness = fitness(j); end end fit_gen(i) = global_fitness; avgfitness_gen(i) = sum(fitness)/pso_option.sizepop; end %% 结果分析 figure; hold on; plot(-fit_gen,'r*-','LineWidth',1.5); plot(-avgfitness_gen,'o-','LineWidth',1.5); legend('最佳适应度','平均适应度',3); xlabel('进化代数','FontSize',10); ylabel('适应度','FontSize',10); grid on; % print -dtiff -r600 pso bestc = global_x(1); bestg = global_x(2); bestCVaccuarcy = -fit_gen(pso_option.maxgen); line1 = 'PSO optimize LSSVM-Classification model'; line2 = ['(参数c1=',num2str(pso_option.c1), ... ',c2=',num2str(pso_option.c2),',终止代数=', ... num2str(pso_option.maxgen),',种群数量pop=', ... num2str(pso_option.sizepop),')']; line3 = ['Best c=',num2str(bestc),' g=',num2str(bestg), ... ' PSO-cvaccuracy=',num2str(bestCVaccuarcy),'%']; title({line1;line2;line3},'FontSize',10);