Multi-dimensional time series | MATLAB implements GWO-BP multi-variable time series prediction (gray wolf algorithm optimizes BP neural network)

Multi-dimensional time series | MATLAB implements GWO-BP multi-variable time series prediction (gray wolf algorithm optimizes BP neural network)

Effect list

basic introduction

1. MATLAB implements GWO-BP multi-variable time series prediction (gray wolf algorithm optimizes BP neural network);
2. The running environment is Matlab2018b;
3. Input multiple features and output a single variable, considering the influence of historical features, multi-variable time series Prediction;
4. data is the data set, GWO_BPNTS.m is the main program, just run it, all files are placed in one folder;
5. The command window outputs R2, MSE, MAE, MAPE and MBE multi-index evaluation;

programming

• Complete program and data download: Private message blogger replied to MATLAB to implement GWO-BP multi-variable time series prediction (gray wolf algorithm optimizes BP neural network) .

%%  优化算法初始化
Alpha_pos = zeros(1, dim);  % 初始化Alpha狼的位置
Alpha_score = inf;          % 初始化Alpha狼的目标函数值,将其更改为-inf以解决最大化问题

Beta_pos = zeros(1, dim);   % 初始化Beta狼的位置
Beta_score = inf;           % 初始化Beta狼的目标函数值 ,将其更改为-inf以解决最大化问题

Delta_pos = zeros(1, dim);  % 初始化Delta狼的位置
Delta_score = inf;          % 初始化Delta狼的目标函数值,将其更改为-inf以解决最大化问题

%%  初始化搜索狼群的位置
Positions = initialization(SearchAgents_no, dim, ub, lb);

%%  用于记录迭代曲线
Convergence_curve = zeros(1, Max_iteration);
%%  循环计数器
iter = 0;

%%  优化算法主循环
while iter < Max_iteration           % 对迭代次数循环
    for i = 1 : size(Positions, 1)   % 遍历每个狼

        % 返回超出搜索空间边界的搜索狼群
        % 若搜索位置超过了搜索空间，需要重新回到搜索空间
        Flag4ub = Positions(i, :) > ub;
        Flag4lb = Positions(i, :) < lb;

        % 若狼的位置在最大值和最小值之间，则位置不需要调整，若超出最大值，最回到最大值边界
        % 若超出最小值，最回答最小值边界
        Positions(i, :) = (Positions(i, :) .* (~(Flag4ub + Flag4lb))) + ub .* Flag4ub + lb .* Flag4lb;   

        % 计算适应度函数值
%         Positions(i, 2) = round(Positions(i, 2));
%         fitness = fical(Positions(i, :));
          fitness = fobj(Positions(i, :));
        % 更新 Alpha, Beta, Delta
        if fitness < Alpha_score           % 如果目标函数值小于Alpha狼的目标函数值
            Alpha_score = fitness;         % 则将Alpha狼的目标函数值更新为最优目标函数值
            Alpha_pos = Positions(i, :);   % 同时将Alpha狼的位置更新为最优位置
        end

        if fitness > Alpha_score && fitness < Beta_score   % 如果目标函数值介于于Alpha狼和Beta狼的目标函数值之间
            Beta_score = fitness;                          % 则将Beta狼的目标函数值更新为最优目标函数值
            Beta_pos = Positions(i, :);                    % 同时更新Beta狼的位置
        end

        if fitness > Alpha_score && fitness > Beta_score && fitness < Delta_score  % 如果目标函数值介于于Beta狼和Delta狼的目标函数值之间
            Delta_score = fitness;                                                 % 则将Delta狼的目标函数值更新为最优目标函数值
            Delta_pos = Positions(i, :);                                           % 同时更新Delta狼的位置
        end

    end

References

[1] https://blog.csdn.net/kjm13182345320/article/details/128163536?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128151206?spm=1001.2014.3001.5502