鲸鱼算法求解TSP.rar（代码完整，数据齐全）

% The Whale Optimization Algorithm function [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,dim,lb,ub,x,y,d0) % initialize position vector and score for the leader Leader_pos=zeros(1,1); 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=1;% 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 for ij = 1:dim if Positions(i,ij)>ub(ij); Positions(i,ij)=(ub(ij)-lb(ij))*rand()+lb(ij); elseif Positions(i,ij)<lb(ij); Positions(i,ij)=(ub(ij)-lb(ij))*rand()+lb(ij); end end % Calculate objective function for each search agent fitness=fun(Positions(i,:),x,y,d0); % 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=2.1*rand(); % r1 is a random number in [0,1] r2=2.1*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<0.9 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>=0.9 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 Convergence_curve(t)=Leader_score; % [t Leader_score] if rand()<0.5; n1 = randperm(20); n2 = 30; n1 = n1(1); xfunt = funt(Leader_pos(n1:n2),Leader_pos(n1:n2)); yfunt = Leader_pos(n1:n2); Leader_pos(n1:n2) = yfunt(xfunt); end Leader_score1 = fun(Leader_pos,x,y,d0); if Leader_score1<Leader_score Leader_score=Leader_score1; Convergence_curve(t)=Leader_score; Positions(i,:)=Leader_pos; end end t=t+1; end % Leader_pos = funt(x,y); [~,Leader_pos] = sort(Leader_pos);