时序预测 | MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测

时序预测 | MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测

效果一览

基本介绍

1.MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测；
2.单变量时间序列预测；
3.运行环境Matlab2018及以上,运行主程序main即可，其余为函数文件无需运行，所有程序放在一个文件夹，data为数据集；
4.SSA-ELM麻雀算法优化极限学习机权值和偏置，命令窗口输出RMSE、MAE、R2、MAPE等评价指标。

程序设计

• 完整程序和数据下载方式1(资源处直接下载)：MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测
• 完整程序和数据下载方式2(订阅《ELM极限学习机》专栏，同时可阅读《ELM极限学习机》专栏收录的所有内容，数据订阅后私信我获取)：MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测
• 完整程序和数据下载方式3(订阅《智能学习》专栏，同时获取《智能学习》专栏收录程序5份，数据订阅后私信我获取)：MATLAB实现GWO-ELM灰狼优化算法优化极限学习机时间序列预测

% Grey Wolf Optimizer
function [Best_pos,Best_score,curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% initialize alpha, beta, and delta_pos
Best_pos=zeros(1,dim);
Best_score=inf; %change this to -inf for maximization problems

Beta_pos=zeros(1,dim);
Beta_score=inf; %change this to -inf for maximization problems

Delta_pos=zeros(1,dim);
Delta_score=inf; %change this to -inf for maximization problems

%Initialize the positions of search agents
Positions=initialization(SearchAgents_no,dim,ub,lb);

curve=zeros(1,Max_iter);

l=0;% Loop counter

% Main loop
while l<Max_iter
    for i=1:size(Positions,1)  
        
       % Return back the search agents that go beyond the boundaries of the search space
        Flag4ub=Positions(i,:)>ub;
        Flag4lb=Positions(i,:)<lb;
        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;               
        
        % Calculate objective function for each search agent
        fitness=fobj(Positions(i,:));
        
        % Update Alpha, Beta, and Delta
        if fitness<Best_score 
            Best_score=fitness; % Update alpha
            Best_pos=Positions(i,:);
        end
        
        if fitness>Best_score && fitness<Beta_score 
            Beta_score=fitness; % Update beta
            Beta_pos=Positions(i,:);
        end
        
        if fitness>Best_score && fitness>Beta_score && fitness<Delta_score 
            Delta_score=fitness; % Update delta
            Delta_pos=Positions(i,:);
        end
    end
       
    a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0
    
    % Update the Position of search agents including omegas
    for i=1:size(Positions,1)
        for j=1:size(Positions,2)     
                       
            r1=rand(); % r1 is a random number in [0,1]
            r2=rand(); % r2 is a random number in [0,1]
            
            A1=2*a*r1-a; % Equation (3.3)
            C1=2*r2; % Equation (3.4)
            
            D_alpha=abs(C1*Best_pos(j)-Positions(i,j)); % Equation (3.5)-part 1
            X1=Best_pos(j)-A1*D_alpha; % Equation (3.6)-part 1
                       
            r1=rand();
            r2=rand();
            
            A2=2*a*r1-a; % Equation (3.3)
            C2=2*r2; % Equation (3.4)
            
            D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2
            X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2       
            
            r1=rand();
            r2=rand(); 
            
            A3=2*a*r1-a; % Equation (3.3)
            C3=2*r2; % Equation (3.4)
            
            D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3
            X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3             
            
            Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)
            
        end
    end
    l=l+1;    
    curve(l)=Best_score;
end

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718