基于麻雀算法优化卷积神经网络(SSA-CNN)的回归预测预测，多变量输入模型 优化参数为学习率，批大小batchsize，正

%_________________________________________________________________________% % 麻雀优化算法 % %_________________________________________________________________________% function [Best_score,Best_pos,curve]=SSA(pop,Max_iter,lb,ub,dim,fobj) ST = 0.7;%预警值 PD = 0.4;%发现者的比列，剩下的是加入者0.7 SD = 0.2;%意识到有危险麻雀的比重 PDNumber = round(pop*PD); %发现者数量 SDNumber = round(SD*PD);%意识到有危险麻雀数量 %种群初始化 X0=initialization(pop,dim,ub,lb); X = X0; %计算初始适应度值 fitness = zeros(1,pop); for i = 1:pop fitness(i) = fobj(X(i,:)); end [fitness, index]= sort(fitness);%升排序 BestF = fitness(1); WorstF = fitness(end); GBestF = fitness(1);%全局最优适应度值 for i = 1:pop X(i,:) = X0(index(i),:); end curve=zeros(1,Max_iter); GBestX = X(1,:);%全局最优位置 X_new = X; for i = 1: Max_iter disp(['第',num2str(i),'次迭代']) BestF = fitness(1); WorstF = fitness(end); R2 = rand(1); for j = 1:PDNumber if(R2<ST) X_new(j,:) = X(j,:).*exp(-j/(rand(1)*Max_iter)); else X_new(j,:) = X(j,:) + randn()*ones(1,dim); end end for j = PDNumber+1:pop % if(j>(pop/2)) if(j>(pop - PDNumber)/2 + PDNumber) X_new(j,:)= randn().*exp((X(end,:) - X(j,:))/j^2); else %产生-1，1的随机数 A = ones(1,dim); for a = 1:dim if(rand()>0.5) A(a) = -1; end end AA = A'*inv(A*A'); X_new(j,:)= X(1,:) + abs(X(j,:) - X(1,:)).*AA'; end end Temp = randperm(pop); SDchooseIndex = Temp(1:SDNumber); for j = 1:SDNumber if(fitness(SDchooseIndex(j))>BestF) X_new(SDchooseIndex(j),:) = X(1,:) + randn().*abs(X(SDchooseIndex(j),:) - X(1,:)); elseif(fitness(SDchooseIndex(j))== BestF) K = 2*rand() -1; X_new(SDchooseIndex(j),:) = X(SDchooseIndex(j),:) + K.*(abs( X(SDchooseIndex(j),:) - X(end,:))./(fitness(SDchooseIndex(j)) - fitness(end) + 10^-8)); end end %边界控制 for j = 1:pop for a = 1: dim if length(ub)>1 if(X_new(j,a)>ub(a)) X_new(j,a) =ub(a); end if(X_new(j,a)<lb(a)) X_new(j,a) =lb(a); end else if(X_new(j,a)>ub) X_new(j,a) =ub; end if(X_new(j,a)<lb) X_new(j,a) =lb; end end end end %更新位置 for j=1:pop fitness_new(j) = fobj(X_new(j,:)); end for j = 1:pop if(fitness_new(j) < GBestF) GBestF = fitness_new(j); GBestX = X_new(j,:); end end X = X_new; fitness = fitness_new; %排序更新 [fitness, index]= sort(fitness);%排序 BestF = fitness(1); WorstF = fitness(end); for j = 1:pop X(j,:) = X(index(j),:); end curve(i) = GBestF; disp(['current iteration is: ',num2str(i), ', best fitness is: ', num2str(GBestF)]); end Best_pos =GBestX; %全局最优位置 K Alpha值组合 Best_score = curve(end);%全局最优适应度值 熵值最小 end