基于粒子群算法(PSO)优化高斯过程回归(PSO-GPR)的数据回归预测，matlab代码，多变量输入模型 评价指标包括:R

function [gbest,g,Convergence_curve]=PSO(N,T,lb,ub,dim,fobj) %% 定义粒子群算法参数 % N 种群 T 迭代次数 %% 随机初始化种群 D=dim; %粒子维数 c1=1.5; %学习因子1 c2=1.5; %学习因子2 w=0.8; %惯性权重 Xmax=ub; %位置最大值 Xmin=lb; %位置最小值 Vmax=ub; %速度最大值 Vmin=lb; %速度最小值 %% %%%%%%%%%%%%%%%%初始化种群个体（限定位置和速度）%%%%%%%%%%%%%%%% x=rand(N,D).*(Xmax-Xmin)+Xmin; v=rand(N,D).*(Vmax-Vmin)+Vmin; %%%%%%%%%%%%%%%%%%初始化个体最优位置和最优值%%%%%%%%%%%%%%%%%%% p=x; pbest=ones(N,1); for i=1:N pbest(i)=fobj(x(i,:)); end %%%%%%%%%%%%%%%%%%%初始化全局最优位置和最优值%%%%%%%%%%%%%%%%%% g=ones(1,D); gbest=inf; for i=1:N if(pbest(i)<gbest) g=p(i,:); gbest=pbest(i); end end %%%%%%%%%%%按照公式依次迭代直到满足精度或者迭代次数%%%%%%%%%%%%% for i=1:T i for j=1:N %%%%%%%%%%%%%%更新个体最优位置和最优值%%%%%%%%%%%%%%%%% if (fobj(x(j,:))) <pbest(j) p(j,:)=x(j,:); pbest(j)=fobj(x(j,:)); end %%%%%%%%%%%%%%%%更新全局最优位置和最优值%%%%%%%%%%%%%%% if(pbest(j)<gbest) g=p(j,:); gbest=pbest(j); end %%%%%%%%%%%%%%%%%跟新位置和速度值%%%%%%%%%%%%%%%%%%%%% v(j,:)=w*v(j,:)+c1*rand*(p(j,:)-x(j,:))... +c2*rand*(g-x(j,:)); x(j,:)=x(j,:)+v(j,:); %%%%%%%%%%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%%%%%%%%%% if length(Vmax)==1 for ii=1:D if (v(j,ii)>Vmax) | (v(j,ii)< Vmin) v(j,ii)=rand * (Vmax-Vmin)+Vmin; end if (x(j,ii)>Xmax) | (x(j,ii)< Xmin) x(j,ii)=rand * (Xmax-Xmin)+Xmin; end end else for ii=1:D if (v(j,ii)>Vmax(ii)) | (v(j,ii)< Vmin(ii)) v(j,ii)=rand * (Vmax(ii)-Vmin(ii))+Vmin(ii); end if (x(j,ii)>Xmax(ii)) | (x(j,ii)< Xmin(ii)) x(j,ii)=rand * (Xmax(ii)-Xmin(ii))+Xmin(ii); end end end end %%%%%%%%%%%%%%%%%%%%记录历代全局最优值%%%%%%%%%%%%%%%%%%%%% Convergence_curve(i)=gbest;%记录训练集的适应度值 end