GWO_灰狼算法_混沌反向学习_灰狼_灰狼优化_灰狼优化算法_

% Improved Grey Wolf Optimizer function [Alpha_scores_FGWO,Alpha_pos_FGWO,Convergence_curve1]=FGWO(SearchAgents_no,Max_iter,lb,ub,dim,objfun) global kkk %display('FGWO is optimizing your problem'); % SearchAgents_no=30; % Max_iter=500; % dim=10; % objfun=@(x)sum(x.^2); % ub=1; %/*lower bounds of the parameters. */ % lb=-1;%/*upper bound of the parameters.*/ %Alpha_scores_FGWO = zeros(1,runtime); %for r=1:runtime % initialize alpha, beta, and delta_pos Alpha_pos_FGWO=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 % ub=ones(1,dim)*1; %/*lower bounds of the parameters. */ % lb=ones(1,dim)*(-1);%/*upper bound of the parameters.*/ % Range = repmat((ub-lb),[SearchAgents_no 1]); % Lower = repmat(lb, [SearchAgents_no 1]); % % 在搜索空间初始化食物源在-1和1之间 % Positions = rand(SearchAgents_no,dim) .* Range + Lower; %% cat映射 % %灰狼位置混沌初始化种群 % x=double(zeros(1,dim)); % 定义初始序列 % X0=[0.1,0.3284]; % 初值 % y=x; % z1=rand(1,dim); % z2=rand(1,dim); % % 赋初值 % z1(1)=X0(1); % z2(1)=X0(2); % % for i=1:SearchAgents_no % for j=1:dim % x(i,j)=mod(z1(j)+z2(j),1); % 取余 % y(i,j)=mod(z1(j)+2*z2(j),1); % 取余 % z1(j)= x(i,j); % z2(j)= y(i,j); % end % end % % X=y; % 利用y序列 % % for i=1:SearchAgents_no % for j=1:dim % Positions_1(i,j) = lb+X(i,j)*(ub-lb); % %Positions(i,j) = lb+X(i,j)*(ub-lb); % end % end %% Logistic映射 % X1=double(zeros(1,dim)); % 定义初始序列 % z0 = rand(1,dim); % u=4; % 混沌系统初始条件 % for i=1:SearchAgents_no % for j=1:dim % X1(i,j) = u*z0(j)*(1-z0(j)); % z0(j) = X1(i,j); % end % end % % for i=1:SearchAgents_no % for j=1:dim % Positions_1(i,j) = lb+X1(i,j)*(ub-lb); % end % end %% Fuch映射 X3=double(zeros(1,dim)); z2=rand(1,dim); % 赋初值 z2(1)=0.4835; for i=1:SearchAgents_no for j=1:dim X3(i,j) = cos(1/(z2(j)^2)); %产生混沌序列1 z2(j) = X3(i,j); end end for i=1:SearchAgents_no for j=1:dim Positions_1(i,j) = lb+X3(i,j)*(ub-lb); end end %% Tent映射 %X2=double(zeros(1,dim)); %z1=rand(1,dim); % 赋初值 %z1(1)=0.4835; %for i=1:SearchAgents_no %for j=1:dim %if z1(j)<=0.51 && z1(j)>=0 % X2(i,j)=1/0.51*z1(j); % z1(j)= X2(i,j); % elseif z1(j)<=1 && z1(j)>0.51 % X2(i,j)=1/0.51*(1-z1(j)); % z1(j)= X2(i,j); % end % end %end %for i=1:SearchAgents_no % for j=1:dim % Positions_1(i,j) = lb(j)+X2(i,j)*(ub(j)-lb(j));%求初始解x_i % Positions_1(i,j) = lb+X2(i,j)*(ub-lb); % end %end %% % 反向学习策略 min_number = min(Positions_1(i,:)); max_number = max(Positions_1(i,:)); for i=1:size(Positions_1,1) for j=1:size(Positions_1,2) Positions_2(i,j) = rand()*(min_number+max_number) - Positions_1(i,j);%每个初始解x_i对应的反向解OP_i % Positions_2(i,j) = lb+ub - Positions_1(i,j); end end Positions = [Positions_1;Positions_2];%两种解合并组成新的种群 for z =1: size(Positions,1) fitness1(z,: )=feval(objfun,Positions(z,:));%求适应度函数 end [m,index] = sort(fitness1);%适应度按升序排列 Positions = Positions(index(1:SearchAgents_no),:);%取前no最优的初始解得到新的灰狼初始种群 %% 同随机初始化位置对比 % 在搜索空间初始化食物源在-1和1之间 % ub1=ones(1,dim)*1; %/*lower bounds of the parameters. */ % lb1=ones(1,dim)*(-1);%/*upper bound of the parameters.*/ % Range1 = repmat((ub1-lb1),[SearchAgents_no 1]); % Lower1 = repmat(lb1, [SearchAgents_no 1]); % Positions_old = rand(SearchAgents_no,dim) .* Range1 + Lower1; % figure % h1 = plot(1:SearchAgents_no,x,'+','MarkerSize',4); % xlabel('迭代次数','FontSize',10,'FontName','Times New Roman') % ylabel('Cat混沌序列值','FontSize',10,'FontName','Times New Roman') % 行数为40，列数为30 %此处的初始种群可以加以改进，比如混沌初始化，反向学习策略产生初始种群，已经有佳点集理论应用到这上面了。 %% 引入PSO记忆算法部分 % 群体交流系数 b1 和个体记忆系数 b2,是[0,1]之间的常数 b1 = 0.5; b2 = 0.5; p = 0.2;% 优胜劣汰选择概率 % 加入个体记忆 pBestScore=zeros(SearchAgents_no); pBest=zeros(SearchAgents_no,dim); % noP * dim l=1;% Loop counter for i=1:SearchAgents_no pBestScore(i)=inf; end %引入部分结束。 % Main loop while l<=Max_iter % 种群适应度和边界初始化 for i=1:size(Positions,1) % 其实是SearchAgents_no 种群个数 ，i 表示每一个灰狼个体 % 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 % 适应度函数 % fitness=fobj(Positions(i,:)); fitness = feval(objfun,Positions(i,:)); % 个体最优保存 if(pBestScore(i)>fitness) pBestScore(i)=fitness; pBest(i,:)=Positions(i,:); end % Update Alpha, Beta, and Delta % 更新 Alpha, Beta, and Delta 位置 if fitness<Alpha_score Alpha_score=fitness; % Update alpha Alpha_pos_FGWO=Positions(i,:); end fitness_Alpha = feval(objfun,Alpha_pos_FGWO); %fitness_Alpha的值是为了进行后面计算权重系数 % 关键是这个优先顺序 if fitness>Alpha_score && fitness<Beta_score Beta_score=fitness; % Update beta Beta_pos=Positions(i,:); end fitness_Beta = feval(objfun,Beta_pos); %同fitness_Alpha if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score Delta_score=fitness; % Update delta Delta_pos=Positions(i,:); end fitness_Delta = feval(objfun,Delta_pos);%同fitness_Alpha end %% 利用Cat混沌局部搜索 min_X = min(Alpha_pos_FGWO); max_X = max(Alpha_pos_FGWO); for j=1:size(Alpha_pos_FGWO,2) Z0(j) = (Alpha_pos_FGWO(j)-min_X)/(max_X-min_X);% 将最优位置映射到（0,1）之间 end % 进行Tent映射迭代 M=double(zeros(1,dim)); % 定义初始序列 Z1=[0.1,0.4835]; % 初值 N=M; z1=rand(1,dim); z2=rand(1,dim); % 赋初值 z1(1)=Z1(1); z2(1)=Z1(2); % 设置混沌迭代次数C_max C_max=50; for t=1:C_max for j=1:size(Alpha_pos_FGWO,2) M(t,j)=mod(z1(j)+z2(j),1); % 取余 N(t,j)=mod(z1(j)+2*z2(j),1); % 取余 z1(j)= M(t,j); z2(j)= N(t,j); end end V=N(t,:); % 得到第t次迭代混度搜索的序列（y序列） % 下面开始进行载波到原解空间，产生最优解的邻域解 for j=1:size(Alpha_pos_FGWO,2) %Alpha_pos_new(j) = Alpha_pos(j)+ (max_X-min_X)*(2*V(j)-1)/2;% 将最优位置映射到（0,1）之间 Alpha_pos_new(j) = Alpha_pos_FGWO(j)+ 3*(max_X-min_X)*(2*V(j)-1)/10;% 将最优位置映射到（0,1）之间 end % 与原来的解相比较，保留最优解 if feval(objfun,Alpha_pos_new)>feval(objfun,Alpha_pos_FGWO) Alpha_pos_FGWO=Alpha_pos_FGWO; else Alpha_pos_FGWO=Alpha_pos_new;