神经网络优化鲸鱼算法
%_________________________________________________________________________% % Whale Optimization Algorithm (WOA) source codes demo 1.0 % % % % % %_________________________________________________________________________% % You can simply define your cost in a seperate file and load its handle to fobj % The initial parameters that you need are: %__________________________________________ % fobj = @YourCostFunction % dim = number of your variables % Max_iteration = maximum number of generations % SearchAgents_no = number of search agents % lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n % ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n % If all the variables have equal lower bound you can just % define lb and ub as two single number numbers % To run ALO: [Best_score,Best_pos,cg_curve]=ALO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj) % The Whale Optimization Algorithm function [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj,handles,value) % initialize position vector and score for the leader Leader_pos=zeros(1,dim); Leader_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); t=0;% Loop counter % Main loop while t<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,:)); All_fitness(1,i)=fitness; % Update the leader if fitness<Leader_score % Change this to > for maximization problem Leader_score=fitness; % Update alpha Leader_pos=Positions(i,:); end end a=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3) % a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12) a2=-1+t*((-1)/Max_iter); % Update the Position of search agents for i=1:size(Positions,1) r1=rand(); % r1 is a random number in [0,1] r2=rand(); % r2 is a random number in [0,1] A=2*a*r1-a; % Eq. (2.3) in the paper C=2*r2; % Eq. (2.4) in the paper b=1; % parameters in Eq. (2.5) l=(a2-1)*rand+1; % parameters in Eq. (2.5) p = rand(); % p in Eq. (2.6) for j=1:size(Positions,2) if p<0.5 if abs(A)>=1 rand_leader_index = floor(SearchAgents_no*rand()+1); X_rand = Positions(rand_leader_index, :); D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7) Positions(i,j)=X_rand(j)-A*D_X_rand; % Eq. (2.8) elseif abs(A)<1 D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1) Positions(i,j)=Leader_pos(j)-A*D_Leader; % Eq. (2.2) end elseif p>=0.5 distance2Leader=abs(Leader_pos(j)-Positions(i,j)); % Eq. (2.5) Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j); end end end t=t+1; Convergence_curve(t)=Leader_score; if t>2 line([t-1 t], [Convergence_curve(t-1) Convergence_curve(t)],'Color','b') xlabel('Iteration'); ylabel('Best score obtained so far'); drawnow end set(handles.itertext,'String', ['The current iteration is ', num2str(t)]) set(handles.optimumtext,'String', ['The current optimal value is ', num2str(Leader_score)]) if value==1 hold on scatter(t*ones(1,SearchAgents_no),All_fitness,'.','k') end end