加权向量算法(INFO)优化广义神经网络的数据回归预测，INFO-GRNN回归预测，多输入单输出模型 评价指标包括:R2、M

%------------------------------------------------------------------------------------------------------------------------- function [Best_pos,Best_Cost,curve]=INFO(pop,Max_iter,lb,ub,dim,fobj) %% Initialization Cost=zeros(pop,1); M=zeros(pop,1); X=initialization(pop,dim,ub,lb); for i=1:pop Cost(i) = fobj(X(i,:)); M(i)=Cost(i); end [~, ind]=sort(Cost); Best_pos = X(ind(1),:); Best_Cost = Cost(ind(1)); Worst_Cost = Cost(ind(end)); Worst_X = X(ind(end),:); I=randi([2 5]); Better_X=X(ind(I),:); Better_Cost=Cost(ind(I)); %% Main Loop of INFO for it=1:Max_iter it alpha=2*exp(-4*(it/Max_iter)); % Eqs. (5.1) & % Eq. (9.1) M_Best=Best_Cost; M_Better=Better_Cost; M_Worst=Worst_Cost; for i=1:pop % Updating rule stage del=2*rand*alpha-alpha; % Eq. (5) sigm=2*rand*alpha-alpha; % Eq. (9) % Select three random solution A1=randperm(pop); A1(A1==i)=[]; a=A1(1);b=A1(2);c=A1(3); e=1e-25; epsi=e*rand; omg = max([M(a) M(b) M(c)]); MM = [(M(a)-M(b)) (M(a)-M(c)) (M(b)-M(c))]; W(1) = cos(MM(1)+pi)*exp(-(MM(1))/omg); % Eq. (4.2) W(2) = cos(MM(2)+pi)*exp(-(MM(2))/omg); % Eq. (4.3) W(3)= cos(MM(3)+pi)*exp(-(MM(3))/omg); % Eq. (4.4) Wt = sum(W); WM1 = del.*(W(1).*(X(a,:)-X(b,:))+W(2).*(X(a,:)-X(c,:))+ ... % Eq. (4.1) W(3).*(X(b,:)-X(c,:)))/(Wt+1)+epsi; omg = max([M_Best M_Better M_Worst]); MM = [(M_Best-M_Better) (M_Best-M_Better) (M_Better-M_Worst)]; W(1) = cos(MM(1)+pi)*exp(-MM(1)/omg); % Eq. (4.7) W(2) = cos(MM(2)+pi)*exp(-MM(2)/omg); % Eq. (4.8) W(3) = cos(MM(3)+pi)*exp(-MM(3)/omg); % Eq. (4.9) Wt = sum(W); WM2 = del.*(W(1).*(Best_pos-Better_X)+W(2).*(Best_pos-Worst_X)+ ... % Eq. (4.6) W(3).*(Better_X-Worst_X))/(Wt+1)+epsi; % Determine MeanRule r = unifrnd(0.1,0.5); MeanRule = r.*WM1+(1-r).*WM2; % Eq. (4) if rand<0.5 z1 = X(i,:)+sigm.*(rand.*MeanRule)+randn.*(Best_pos-X(a,:))/(M_Best-M(a)+1); z2 = Best_pos+sigm.*(rand.*MeanRule)+randn.*(X(a,:)-X(b,:))/(M(a)-M(b)+1); else % Eq. (8) z1 = X(a,:)+sigm.*(rand.*MeanRule)+randn.*(X(b,:)-X(c,:))/(M(b)-M(c)+1); z2 = Better_X+sigm.*(rand.*MeanRule)+randn.*(X(a,:)-X(b,:))/(M(a)-M(b)+1); end % Vector combining stage u=zeros(1,dim); for j=1:dim mu = 0.05*randn; if rand <0.5 if rand<0.5 u(j) = z1(j) + mu*abs(z1(j)-z2(j)); % Eq. (10.1) else u(j) = z2(j) + mu*abs(z1(j)-z2(j)); % Eq. (10.2) end else u(j) = X(i,j); % Eq. (10.3) end end % Local search stage if rand<0.5 L=rand<0.5;v1=(1-L)*2*(rand)+L;v2=rand.*L+(1-L); % Eqs. (11.5) & % Eq. (11.6) Xavg=(X(a,:)+X(b,:)+X(c,:))/3; % Eq. (11.4) phi=rand; Xrnd = phi.*(Xavg)+(1-phi)*(phi.*Better_X+(1-phi).*Best_pos); % Eq. (11.3) Randn = L.*randn(1,dim)+(1-L).*randn; if rand<0.5 u = Best_pos + Randn.*(MeanRule+randn.*(Best_pos-X(a,:))); % Eq. (11.1) else u = Xrnd + Randn.*(MeanRule+randn.*(v1*Best_pos-v2*Xrnd)); % Eq. (11.2) end end % Check if new solution go outside the search space and bring them back New_X= BC(u,lb,ub); New_Cost = fobj(New_X); if New_Cost<Cost(i) X(i,:)=New_X; Cost(i)=New_Cost; M(i)=Cost(i); if Cost(i)<Best_Cost Best_pos=X(i,:); Best_Cost = Cost(i); end end end % Determine the worst solution [~, ind]=sort(Cost); Worst_X=X(ind(end),:); Worst_Cost=Cost(ind(end)); % Determine the better solution I=randi([2 5]); Better_X=X(ind(I),:); Better_Cost=Cost(ind(I)); % Update Convergence_curve curve(it)=Best_Cost; avcurve(it)=sum(curve)/length(curve); % Show Iteration Information %disp(['Iteration ' num2str(it) ',: Best Cost = ' num2str(Best_Cost)]); end end function X = BC(X,lb,ub) Flag4ub=X>ub; Flag4lb=X<lb; X=(X.*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; end