基于鹈鹕算法(POA)优化径向基神经网络(POA-RBF)的数据回归预测，多变量输入模型 matlab代码，优化参数为扩散速

function[Best_score,Best_pos,POA_curve]=POA(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness) lowerbound=ones(1,dimension).*(lowerbound); % Lower limit for variables upperbound=ones(1,dimension).*(upperbound); % Upper limit for variables %% INITIALIZATION for i=1:dimension X(:,i) = lowerbound(i)+rand(SearchAgents,1).*(upperbound(i) - lowerbound(i)); % Initial population end for i =1:SearchAgents L=X(i,:); fit(i)=fitness(L); end %% for t=1:Max_iterations t %% update the best condidate solution [best , location]=min(fit); if t==1 Xbest=X(location,:); % Optimal location fbest=best; % The optimization objective function elseif best<fbest fbest=best; Xbest=X(location,:); end %% UPDATE location of food X_FOOD=[]; k=randperm(SearchAgents,1); X_FOOD=X(k,:); F_FOOD=fit(k); %% for i=1:SearchAgents %% PHASE 1: Moving towards prey (exploration phase) I=round(1+rand(1,1)); if fit(i)> F_FOOD X_new=X(i,:)+ rand(1,1).*(X_FOOD-I.* X(i,:)); %Eq(4) else X_new=X(i,:)+ rand(1,1).*(X(i,:)-1.*X_FOOD); %Eq(4) end X_new= max(X_new,lowerbound);X_new = min(X_new,upperbound); % Updating X_i using (5) f_new = fitness(X_new); if f_new <= fit (i) X(i,:) = X_new; fit (i)=f_new; end %% END PHASE 1: Moving towards prey (exploration phase) %% PHASE 2: Winging on the water surface (exploitation phase) X_new=X(i,:)+0.2*(1-t/Max_iterations).*(2*rand(1,dimension)-1).*X(i,:);% Eq(6) X_new= max(X_new,lowerbound);X_new = min(X_new,upperbound); % Updating X_i using (7) f_new = fitness(X_new); if f_new <= fit (i) X(i,:) = X_new; fit (i)=f_new; end %% END PHASE 2: Winging on the water surface (exploitation phase) end best_so_far(t)=fbest; average(t) = mean (fit); end Best_score=fbest; Best_pos=Xbest; POA_curve=best_so_far; end