鲸鱼算法(WOA)优化长短期记忆神经网络的数据回归预测，WOA-LSTM回归预测，多输入单输出模型 评价指标包括:R2、MAE

%_________________________________________________________________________% % Whale Optimization Algorithm (WOA) source codes demo 1.0 % % % % Developed in MATLAB R2011b(7.13) % % % % Author and programmer: Seyedali Mirjalili % % % % e-Mail: [email protected] % % [email protected] % % % % Homepage: http://www.alimirjalili.com % % % % Main paper: S. Mirjalili, A. Lewis % % The Whale Optimization Algorithm, % % Advances in Engineering Software , in press, % % DOI: http://dx.doi.org/10.1016/j.advengsoft.2016.01.008 % % % %_________________________________________________________________________% % The Whale Optimization Algorithm function [Best_Cost,Best_pos,curve]=WOA(pop,Max_iter,lb,ub,dim,fobj) % initialize position vector and score for the leader Best_pos=zeros(1,dim); Best_Cost=inf; %change this to -inf for maximization problems %Initialize the positions of search agents Positions=initialization(pop,dim,ub,lb); 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,:)); % Update the leader if fitness<Best_Cost % Change this to > for maximization problem Best_Cost=fitness; % Update alpha Best_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(pop*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*Best_pos(j)-Positions(i,j)); % Eq. (2.1) Positions(i,j)=Best_pos(j)-A*D_Leader; % Eq. (2.2) end elseif p>=0.5 distance2Leader=abs(Best_pos(j)-Positions(i,j)); % Eq. (2.5) Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Best_pos(j); end end end t=t+1; curve(t)=Best_Cost; [t Best_Cost] end