智能优化算法 - 灰狼优化算法（Python 和 Matlab 实现）

最编程 2024-04-17 20:13:10

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% 主程序 GWO clear close all clc %%完整代码见微信公众号：电力系统与算法之美 %输入关键字：灰狼算法 SearchAgents_no = 30 ; %　种群规模 dim = 10 ; % 粒子维度 Max_iter = 1000 ; %　迭代次数 ub = 5 ; lb = -5 ; %% 初始化三匹头狼的位置 Alpha_pos=zeros(1,dim); Alpha_score=inf; Beta_pos=zeros(1,dim); Beta_score=inf; Delta_pos=zeros(1,dim); Delta_score=inf; Convergence_curve = zeros(Max_iter,1); %% 开始循环 for l=1:Max_iter for i=1:size(Positions,1) %% 返回超出搜索空间边界的搜索代理 Flag4ub=Positions(i,:)>ub; Flag4lb=Positions(i,:)<lb; Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; %% 计算每个搜索代理的目标函数 fitness=sum(Positions(i,:).^2); %% 更新 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 %% 更新搜索代理的位置，包括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 Convergence_curve(l)=Alpha_score; disp(['Iteration = ' num2str(l) ', Evaluations = ' num2str(Alpha_score)]); end %========可视化============== figure('unit','normalize','Position',[0.3,0.35,0.4,0.35],'color',[1 1 1],'toolbar','none') %% 目标空间 subplot(1,2,1); x = -5:0.1:5;y=x; L=length(x); f=zeros(L,L); for i=1:L for j=1:L f(i,j) = x(i)^2+y(j)^2; end end surfc(x,y,f,'LineStyle','none'); xlabel('x_1'); ylabel('x_2'); zlabel('F') title('Objective space') %% 狼群算法 subplot(1,2,2); semilogy(Convergence_curve,'Color','r','linewidth',1.5) title('Convergence_curve') xlabel('Iteration'); ylabel('Best score obtained so far'); axis tight grid on box on legend('GWO') display(['The best solution obtained by GWO is : ', num2str(Alpha_pos)]); display(['The best optimal value of the objective funciton found by GWO is : ', num2str(Alpha_score)]);

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