粒子群算法(PSO)优化核极限学习机(KELM)的分类预测，多特征输入模型 PSO-KELM分类预测模型 多特征输入单输出的

function [Destination_fitness,bestPositions,Convergence_curve]=ismaforkelm(N,Max_iter,lb,ub,dim,fobj) % 种群初始化 bestPositions=zeros(1,dim); Destination_fitness=inf;%设置初始全最优适应度 AllFitness = inf*ones(N,1);%初始所有种群的适应度 weight = ones(N,dim);%每一个黏菌的权重 %种群初始化 lb = [1,1];%下边界 ub = [10,10];%上边界 Ub=ub(1);Lb=lb(1); X=Goodnode_initialization(N,dim,Ub,Lb); it=1; %初始迭代次数 lb=ones(1,dim).*lb; % 下界 ub=ones(1,dim).*ub; % 上界 z=0.03; % 初始参数z % 主循环 while it <= Max_iter it for i=1:N % 检查是否在范围内 Flag4ub=X(i,:)>ub; Flag4lb=X(i,:)<lb; X(i,:)=(X(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; AllFitness(i) = fobj(X(i,:)); end [X,AllFitness]=ODE(X,AllFitness,ub,lb,fobj); if AllFitness(1) < Destination_fitness bestPositions=X(1,:); Destination_fitness = AllFitness(1); end [SmellOrder,SmellIndex] = sort(AllFitness); %%筛选出最优和最差的种群 worstFitness = SmellOrder(N); bestFitness = SmellOrder(1); % bestPositions=X(SmellIndex(1),:); % Destination_fitness = AllFitness(SmellIndex(1)); S=bestFitness-worstFitness+eps; %避免分母为0的操作 %计算每一个黏菌的权重 for i=1:N for j=1:dim if i<=(N/2) %参考源码，式2.5 weight(SmellIndex(i),j) = 1+rand()*log10((bestFitness-SmellOrder(i))/(S)+1); else weight(SmellIndex(i),j) = 1-rand()*log10((bestFitness-SmellOrder(i))/(S)+1); end end end %更新当前最优的种群和适应度 if bestFitness < Destination_fitness bestPositions=X(SmellIndex(1),:); Destination_fitness = bestFitness; end a = atanh(-(it/Max_iter)+1); %参考原式2.4 b = 1-it/Max_iter; % 更新每一代种群的位置 for i=1:N if rand<z %Eq.(2.7) X(i,:) = (ub-lb)*rand+lb; else p =tanh(abs(AllFitness(i)-Destination_fitness)); %Eq.(2.2) vb = unifrnd(-a,a,1,dim); %Eq.(2.3) vc = unifrnd(-b,b,1,dim); for j=1:dim r = rand(); A = randi([1,N]); % two positions randomly selected from population B = randi([1,N]); if r<p %Eq.(2.1) X(i,j) = bestPositions(j)+ vb(j)*(weight(i,j)*X(A,j)-X(B,j)); else X(i,j) = vc(j)*X(i,j); end end end end f_ave=mean(AllFitness); for g=1:N if fobj(X(i,:))<f_ave X(i,:)=X(i,:).*trnd(it); X(i,:) = Bounds(X(i,:), lb, ub); else rand_num=randperm(0.5*N,1); X(i,:)=(X(i,:)+X(rand_num,:))/2; X(i,:) = Bounds(X(i,:), lb, ub); end end Convergence_curve1(it)=Destination_fitness; it=it+1; end