北方苍鹰算法(NGO)优化长短期记忆神经网络的数据分类预测，NGO-LSTM分类预测，多输入单输出模型 多特征输入单输出的二

function [Score,Best_pos,NGO_curve]=NGO(Search_Agents,Max_iterations,lb,ub,dim,fobj) dimensions=dim; Upperbound=ub; Lowerbound=lb; X0=initialization(Search_Agents,dim,ub,lb); X = X0; X_new=[]; fit=[]; fit_new=[]; NGO_curve=zeros(1,Max_iterations); %% for i =1:Search_Agents L=X(i,:); fit(i)=fobj(L); % Fitness evaluation (Explained at the top of the page. ) end for t=1:Max_iterations % algorithm iteration disp(['第',num2str(t),'次迭代']) %% update: BEST proposed solution [best , blocation]=min(fit); if t==1 xbest=X(blocation,:); % Optimal location fbest=best; % The optimization objective function elseif best<fbest fbest=best; xbest=X(blocation,:); end %% UPDATE Northern goshawks based on PHASE1 and PHASE2 for i=1:Search_Agents %% Phase 1: Exploration I=round(1+rand); k=randperm(Search_Agents,1); P=X(k,:); % Eq. (3) F_P=fit(k); if fit(i)> F_P X_new(i,:)=X(i,:)+rand(1,dimensions) .* (P-I.*X(i,:)); % Eq. (4) else X_new(i,:)=X(i,:)+rand(1,dimensions) .* (X(i,:)-P); % Eq. (4) end X_new(i,:) = max(X_new(i,:),Lowerbound);X_new(i,:) = min(X_new(i,:),Upperbound); % update position based on Eq (5) L=X_new(i,:); fit_new(i)=fobj(L); if(fit_new(i)<fit(i)) X(i,:) = X_new(i,:); fit(i) = fit_new(i); end %% END PHASE 1 %% PHASE 2 Exploitation R=0.02*(1-t/Max_iterations);% Eq.(6) X_new(i,:)= X(i,:)+ (-R+2*R*rand(1,dimensions)).*X(i,:);% Eq.(7) X_new(i,:) = max(X_new(i,:),Lowerbound);X_new(i,:) = min(X_new(i,:),Upperbound); % update position based on Eq (8) L=X_new(i,:); fit_new(i)=fobj(L); if(fit_new(i)<fit(i)) X(i,:) = X_new(i,:); fit(i) = fit_new(i); end %% END PHASE 2 end% end for i=1:N %% %% SAVE BEST SCORE best_so_far(t)=fbest; % save best solution so far average(t) = mean (fit); Score=fbest; Best_pos=xbest; NGO_curve(t)=Score; end Best_pos(2)=round(Best_pos(2)); %% end