灰狼算法优化数据处理组合方法GWO-GMDH时序数据预测【含Matlab源码 3246期】.zip

clear all; close all; clc; addpath('工具箱\') %% 导入数据并预处理 data = xlsread('工作簿4.xlsx'); data =data(:,1)'; dataSmoothed = smooth(data, 25); % 使用滑动平均进行去噪 Delays = [1 2 3 4 5];%延迟五个值 [Inputs, Targets] = CreateTimeSeriesData(dataSmoothed',Delays);% 创建时间序列数据 nData = size(Inputs,2); %输入样本数量 %Perm = randperm(nData); %随机取样开关 Perm = 1:nData; %% 训练数据 pTrain = 0.8;%训练集80% nTrainData = round(pTrain*nData); %训练集样本个数 TrainInd = Perm(1:nTrainData); TrainInputs = Inputs(:,TrainInd); %训练输入 TrainTargets = Targets(:,TrainInd);%训练输出 %% Test Data pTest = 1 - pTrain;%测试集 nTestData = nData - nTrainData; %测试集样本个数 TestInd = Perm(nTrainData+1:end); TestInputs = Inputs(:,TestInd); %测试输入 TestTargets = Targets(:,TestInd);%测试输出 save data SearchAgents_no = 5; % 种群数量 5 Function_name='GMDH_MIN'; Max_iter = 5; % 迭代次数 5 % Load details of the selected benchmark function lb = [10 0.01 2 ];%三个参数的下限 ub = [100 1 10 ];%三个参数的上限 dim = 3;%数量 fobj = @(x)GMDH_MIN(x); %% GWO 优化代码 % 初始化α,β,Delta_pos 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,change this to -inf for maximization problems 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,change this to -inf for maximization problems 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,change this to -inf for maximization problems %初始化的位置搜索 Positions=initialization(SearchAgents_no,dim,ub,lb); Convergence_curve=zeros(1,Max_iter); K=zeros(1,Max_iter); l=0;% 循环计数器 % 主循环 while l<Max_iter % 对迭代次数循环 for i=1:size(Positions,1) % 遍历每个狼 %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; %~表示取反 % 计算适应度函数值 fitness=fobj(Positions(i,:)); % 更新Alpha, Beta和Delta的值 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,:);%则将Alpha狼的目标函数值更新为最优目标函数值 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 K=Alpha_score; end a=2-2*((i)/Max_iter); % 对每一次迭代，计算相应的a值，a decreases linearly fron 2 to 0 %a=2-2*((1/(exp(1)-1))*(exp(i/Max_iter)-1)); % 包围猎物，位置更新 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; % 计算系数A，Equation (3.3) C1=2*r2; % 计算系数C，Equation (3.4) % Alpha狼位置更新 D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1 X1=Alpha_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) % Beta狼位置更新 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; % 计算系数A，Equation (3.3) C3=2*r2; %计算系数C， Equation (3.4) % Delta狼位置更新 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 % 位置更新 Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7) end end l=l+1; Convergence_curve(l)=Alpha_score; end %% 优化结果导入代码 Alpha_pos(1,1)=round(Alpha_pos(1,1)); Alpha_pos(1,3)=round(Alpha_pos(1,3)); [cost1,Outputs1] = GMDH_MIN(Alpha_pos) %优化后 [cost0,Outputs0] = GMDH_MIN([25,0,5]) %优化前 load data TrainInd TestInd TrainTargets TestTargets Targets Outputs2=cell2mat(Outputs1); TrainOutputs2 = Outputs2(:,TrainInd); TestOutputs2 = Outputs2(:,TestInd); Outputs3=cell2mat(Outputs0); TrainOutputs3 = Outputs3(:,TrainInd); TestOutputs3 = Outputs3(:,TestInd); %% 结果展示 figure;%优化前 PlotResults(TestTargets, TestOutputs3, '优化前Test Data'); figure plotregression(TrainTargets, TrainOutputs3); xlabel('优化前训练集') figure plotregression(TestTargets, TestOutputs3); xlabel('优化前测试集') figure;% 优化后 PlotResults(TestTargets, TestOutputs2, '优化后Test Data'); figure plotregression(TrainTargets, TrainOutputs2); xlabel('优化后训练集') figure plotregression(TestTargets, TestOutputs2); xlabel('优化后测试集')