## 2 部分代码

%_________________________________________________________________________%% 鲸鱼优化算法             %%_________________________________________________________________________%% The Whale Optimization Algorithmfunction [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)% 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 agentsPositions=initialization(SearchAgents_no,dim,ub,lb);Convergence_curve=zeros(1,Max_iter);t=0;% Loop counter% Main loopwhile 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<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;end

## 4 参考文献

[1]刘沛津, 胡冀飞, 贺宁,等. 改进鲸鱼算法优化LSSVM的短期电力负荷预测研究[J]. 现代电子技术, 2021, 44(13):5.