考虑多目标粒子群算法的含风光燃储微网优化调度.zip

clear clc close all % psoSVMcgForClass % % by faruto %Email:[email protected] QQ:516667408 http://blog.sina.com.cn/faruto %last modified 2011.06.08 % 若转载请注明： % faruto and liyang , LIBSVM-farutoUltimateVersion % a toolbox with implements for support vector machines based on libsvm, 2011. % Software available at http://www.matlabsky.com % % Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for % support vector machines, 2001. Software available at % http://www.csie.ntu.edu.tw/~cjlin/libsvm %% 参数初始化 pso_option = struct('c1',2.05,'c2',2.05,'maxgen',2500,'sizepop',100, ... 'k',0.6,'wV',1.1,'wP',1.1,'v',5, ... 'popcmax',30,'popcmin',-30,'popgmax',30,'popgmin',-30); % pso_option = struct('c1',1.5,'c2',1.7,'maxgen',200,'sizepop',20, ... % 'k',0.6,'wV',1,'wP',1,'v',5, ... % 'popcmax',100,'popcmin',0.1,'popgmax',10^3,'popgmin',10^(-2)); % 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5,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. Vcmax = pso_option.k*pso_option.popcmax; Vcmin = -Vcmax ; Vgmax = pso_option.k*pso_option.popgmax; Vgmin = -Vgmax ; eps = 10^(-5); % train_1 = train(1:100,1); % train_label1 = train_label(1:100); % train_2 = train(31:40,1:9); % train_label2 = train_label(31:40); %% 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(1,1); V(i,2)=Vgmax*rands(1,1); % 计算初始适应度 fitness(i)=myfunc_fit1(pop(i,:)); % [traini1,a1,b1]=svmpredict(train_label1,train_1,model); % [traini2,a2,b2]=svmpredict(train_label2,train_2,model); % fitness(i)= 0.25*mse(traini1-train_label1) + 0.75*mse(traini2-train_label2); 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) = (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 fitness(j)=myfunc_fit1(pop(j,:)); % [trainj1,a1,b1]=svmpredict(train_label1,train_1,model); % [trainj2,a2,b2]=svmpredict(train_label2,train_2,model); % fitness(j)= 0.2*mse(trainj1-train_label1) + 0.8*mse(trainj2-train_label2); %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 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 xlswrite('fit_gen.xlsx',fit_gen); xlswrite('avgfitness_gen.xlsx',avgfitness_gen); %% 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'); xlabel('进化代数','FontSize',12); ylabel('适应度','FontSize',12); grid on; bestc = global_x(1); bestg = global_x(2); bestCVmse = fit_gen(pso_option.maxgen); line1 = '适应度曲线MSE[PSOmethod]'; 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), ... ' CVmse=',num2str(bestCVmse),]; title({line1;line2;line3},'FontSize',12);