【DELM分类】基于matlab灰狼算法改进深度学习极限学习机GWO-DELM数据分类【含Matlab源码 4909期】.zip

%___________________________________________________________________% % Grey Wolf Optimizer (GWO) source codes version 1.0 % % % % Developed in MATLAB R2011b(7.13) % % % % Author and programmer: Seyedali Mirjalili % % % % e-Mail: [email protected] % % [email protected] % % % % Homepage: http://www.alimirjalili.com % % % % Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis % % Grey Wolf Optimizer, Advances in Engineering % % Software , in press, % % DOI: 10.1016/j.advengsoft.2013.12.007 % % % %___________________________________________________________________% % Grey Wolf Optimizer function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj) % initialize alpha, beta, and delta_pos Alpha_pos=zeros(1,dim); Alpha_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); Convergence_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<Alpha_score Alpha_score=fitness; % Update alpha Alpha_pos=Positions(i,:); end if fitness>Alpha_score && fitness<Beta_score Beta_score=fitness; % Update beta Beta_pos=Positions(i,:); end if fitness>Alpha_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*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) 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) % if Positions(i,j)>ub Positions(i,j)=ub; elseif Positions(i,j)<lb Positions(i,j)=lb; end end end l=l+1; Convergence_curve(l)=Alpha_score; end