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回归预测 | MATLAB实现RUN-XGBoost多输入回归预测

预测效果

基本介绍

MATLAB实现RUN-XGBoost多输入回归预测（完整源码和数据）
1.龙格库塔优化XGBoost，数据为多输入回归数据，输入7个特征，输出1个变量,程序乱码是由于版本不一致导致，可以用记事本打开复制到你的文件。
2.运行环境MATLAB2018b及以上。
3.附赠案例数据可直接运行main一键出图～
4.注意程序和数据放在一个文件夹。
5.代码特点：参数化编程、参数可方便更改、代码编程思路清晰、注释明细。

程序设计

• 完整源码和数据获取方式（资源出下载）：MATLAB实现RUN-XGBoost多输入回归预测。

%% Main Loop of RUN 
it=1;%Number of iterations
while it<Max_iterationit=it+1;f=20.*exp(-(12.*(it/Max_iteration))); % (Eq.17.6) Xavg = mean(X);               % Determine the Average of SolutionsSF=2.*(0.5-rand(1,pop)).*f;    % Determine the Adaptive Factor (Eq.17.5)for i=1:pop[~,ind_l] = min(Cost);lBest = X(ind_l,:);   [A,B,C]=RndX(pop,i);   % Determine Three Random Indices of Solutions[~,ind1] = min(Cost([A B C]));% Determine Delta X (Eqs. 11.1 to 11.3)gama = rand.*(X(i,:)-rand(1,dim).*(ub-lb)).*exp(-4*it/Max_iteration);  Stp=rand(1,dim).*((Best_pos-rand.*Xavg)+gama);DelX = 2*rand(1,dim).*(abs(Stp));% Determine Xb and Xw for using in Runge Kutta methodif Cost(i)<Cost(ind1)                Xb = X(i,:);Xw = X(ind1,:);elseXb = X(ind1,:);Xw = X(i,:);endSM = RungeKutta(Xb,Xw,DelX);   % Search Mechanism (SM) of RUN based on Runge Kutta MethodL=rand(1,dim)<0.5;Xc = L.*X(i,:)+(1-L).*X(A,:);  % (Eq. 17.3)Xm = L.*Best_pos+(1-L).*lBest;   % (Eq. 17.4)vec=[1,-1];flag = floor(2*rand(1,dim)+1);r=vec(flag);                   % An Interger number g = 2*rand;mu = 0.5+.1*randn(1,dim);% Determine New Solution Based on Runge Kutta Method (Eq.18) if rand<0.5Xnew = (Xc+r.*SF(i).*g.*Xc) + SF(i).*(SM) + mu.*(Xm-Xc);elseXnew = (Xm+r.*SF(i).*g.*Xm) + SF(i).*(SM)+ mu.*(X(A,:)-X(B,:));end  % Check if solutions go outside the search space and bring them backFU=Xnew>ub;FL=Xnew<lb;Xnew=(Xnew.*(~(FU+FL)))+ub.*FU+lb.*FL; CostNew=fobj(Xnew);if CostNew<Cost(i)X(i,:)=Xnew;Cost(i)=CostNew;end
%% Enhanced solution quality (ESQ)  (Eq. 19)      if rand<0.5EXP=exp(-5*rand*it/Max_iteration);r = floor(Unifrnd(-1,2,1,1));u=2*rand(1,dim); w=Unifrnd(0,2,1,dim).*EXP;               %(Eq.19-1)[A,B,C]=RndX(pop,i);Xavg=(X(A,:)+X(B,:)+X(C,:))/3;           %(Eq.19-2)         beta=rand(1,dim);Xnew1 = beta.*(Best_pos)+(1-beta).*(Xavg); %(Eq.19-3)for j=1:dimif w(j)<1 Xnew2(j) = Xnew1(j)+r*w(j)*abs((Xnew1(j)-Xavg(j))+randn);elseXnew2(j) = (Xnew1(j)-Xavg(j))+r*w(j)*abs((u(j).*Xnew1(j)-Xavg(j))+randn);endendFU=Xnew2>ub;FL=Xnew2<lb;Xnew2=(Xnew2.*(~if rand<w(randi(dim)) SM = RungeKutta(X(i,:),Xnew2,DelX);Xnew = (Xnew2-rand.*Xnew2)+ SF(i)*(SM+(2*rand(1,dim).*Best_pos-Xnew2));  % (Eq. 20)FU=Xnew>ub;FL=Xnew<lb;Xnew=(Xnew.*(~(FU+FL)))+ub.*FU+lb.*FL;CostNew=fobj(Xnew);if CostNew<Cost(i)X(i,:)=Xnew;Cost(i)=CostNew;endendendend
% End of ESQ         
%% Determine the Best Solutionif Cost(i)<Best_scoreBest_pos=X(i,:);Best_score=Cost(i);endend
% Save Best Solution at each iteration    
curve(it) = Best_score;
disp(['it : ' num2str(it) ', Best Cost = ' num2str(curve(it) )]);endend

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/128577926?spm=1001.2014.3001.5501
[2] https://blog.csdn.net/kjm13182345320/article/details/128573597?spm=1001.2014.3001.5501