## 2 部分代码

%___________________________________________________________________%%  Grey Wold Optimizer (GWO) source codes version 1.0               %%                                                                   %%  Developed in MATLAB R2011b(7.13)                                 %%                                                                   %%  Author and programmer: Seyedali Mirjalili                        %%                                                                   %%         e-Mail: ali.mirjalili@gmail.com                           %%                 seyedali.mirjalili@griffithuni.edu.au             %%                                                                   %%       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 Optimizerfunction [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)% initialize alpha, beta, and delta_posAlpha_pos=zeros(1,dim);Alpha_score=inf; %change this to -inf for maximization problemsBeta_pos=zeros(1,dim);Beta_score=inf; %change this to -inf for maximization problemsDelta_pos=zeros(1,dim);Delta_score=inf; %change this to -inf for maximization problems%Initialize the positions of search agentsPositions=initialization(SearchAgents_no,dim,ub,lb);Convergence_curve=zeros(1,Max_iter);l=0;% Loop counter% Main loopwhile 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)        end    end    l=l+1;        Convergence_curve(l)=Alpha_score;end

## 4 参考文献

[1]罗佳, 唐斌. 新型灰狼优化算法在函数优化中的应用[J]. 兰州理工大学学报, 2016, 42(3):6.