【改进灰狼优化算法】改进收敛因子和比例权重的灰狼优化算法【期刊论文完美复现】（Matlab代码实现）

function [Alpha_score,Alpha_pos,Convergence_curve]=CGWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
%% 收敛因子参数
aintit = 2;
afinal = 0;
% 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
    %% 改进点：收敛因子改进，文献中式（7）
    n = 1;%递减系数
   if(l<0.5*Max_iter)
       a = afinal + (aintit - afinal)*(1 + (cos((l-1)*pi/( Max_iter-1)))^n)/2;
   else
       a = afinal + (aintit - afinal)*(1 - (cos((l-1)*pi/( Max_iter-1)))^n)/2;
   end
    % 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             
           %% 改进点：基于步长欧氏距离的比例权重
           S = abs(X1) + abs(X2)+abs(X3);
           if S~=0 %防止分母为0
               W1 = abs(X1)/(abs(X1) + abs(X2)+abs(X3));
               W2 = abs(X2)/(abs(X1) + abs(X2)+abs(X3));
               W3 = abs(X3)/(abs(X1) + abs(X2)+abs(X3));
           else
               W1=1;W2=1;W3=1;
           end
            Positions(i,j)=(W1*X1+X2*W2+X3*W3)/3;% Equation (3.7)
        end
    end
    l=l+1;    
    Convergence_curve(l)=Alpha_score;
end