ant_algorithm_code

end
% place m ants in n nodes
ant_tours = zeros(m, j1);
t = 1;
while ((t <= t_max))
ant_tours(:,1) = randint(m,1,[1,k1]);
% CREATE TOURS
%%%%=============================================================
for i=index:n+index
if i>n
i=i-n;
end
E(i,:)=[d(i),b(start)];
start=start+1;
if start>n
start=1;
end
end
mincost;
for i = 1 : j1
L = L + e1(P(i),i);
end
L_T(k) = L;
if (L_T(k) < L_T(best_ant))
best_ant = k;
c_tv = length(to_visit);
p = zeros(1,c_tv);
for i = 1 : c_tv
heur_inf(i)=e1_pro(to_visit(i),s);
p(i) = (tau(current_node,to_visit(i),s-1))^alpha *(heur_inf(i))^beta;
orignal_idx=[1:length(measurevalue)];
end
n=max(size(measurevalue2));
E=zeros(n,2);
cost=zeros(1,n);
C=zeros(n,2);
[a,b]=sort(measurevalue2);
[c,d]=sort(predictvalue);
D1=D1+1;
if D1>n
D1=1;
end
end
end
end
[mincost,index]=min(cost);
start=C(index,2);
end
end
for i=1:k1
for j=1:j1
e1_pro(i,j)=e1_pro(i,j)/sum(e1_pro(:,j));%%%%%
end
end
%%%%%% this code uses the ACO algorithm to find the candidates
T_best = T_min;
end
end % ends while
measurevalue2=measurevalue(b1(T_best));
orignal_idx=b1(T_best);
%%%%%
else
measurevalue2=measurevalue;
function assignment=rotationallyfunction2(measurevalue,predictvalue)
%%%%%% this part is to obtain matrix for later use
%n1=max(size(measurevalue));
break;
end
end
city_to_visit = select;
ant_tours(k,s) = city_to_visit;
end
end
L_min = min(L_T);
T_min = ant_tours(best_ant,:);
% update pheromone trails;
for i = 1 : (j1-1)
tau(T_min(i),T_min(i+1),i) = (1 - rho) *tau(T_min(i),T_min(i+1),i) + rho / L_min;
r = rand;
select = to_visit(c_tv);
for i = 1 : c_tv
if (r <= p(i))
select = to_visit(i);
number_of_ants =20;
alpha = 1;
beta = 2;
rho = 0.9;
m = number_of_ants;
t_max = 10;
L_nn=10;
%[L_nn, P_nn] = NearestNeighborTSP(d);
L_best = inf;
T_best = 0;
% INITIALIZATION
%%%===========================================================
% pheromone trails
c = 1 / (j1* L_nn);
for i=1:(j1-1)
tau(:,:,i)=ones(k1,k1) * c-diag(ones(1,k1)*c);
end
% COMPLETE
%================================================================
t = t + 1;
if (L_min < L_best)
L_best = L_min;
tau(current_node,city_to_visit,s-1) = (1 - rho) *tau(current_node,city_to_visit,s-1) + c;
end
end
% UPDATE
%===================================================================
for s = 2 : j1
for k = 1 : m
current_node = ant_tours(k,s-1);
visited = ant_tours(k,:);
to_visit =setdiff([1:k1],visited);
D=C(1,2);
for i=1:n
cost(1)=e(i,D)+cost(1);
D=D+1;
if D>n
D=1;
end
end
for i=2:n
if C(i,2)==(C(i-1,2)+1)
cost(i)=cost(i-1);
end
assignment=E1;
jj=1;
for i=1:n
for jj=1:n
if (E(jj,1)==i)
E1(i,:)=E(jj,:);
end
end
end
for i=1:n
E1(i,2)=orignal_idx(E1(i,2));
else
D1=C(i,2);
for k=i:n+i
if k>n
k=k-n;
end
cost(i)=e(k,D1)+cost(i);
% ant_tours(:,j1+1) = ant_tours(:,1);
L_T = zeros(1,m);
best_ant = 1;
for k = 1 : m
P = ant_tours(k,:);
L = 0;
end
sum_p = sum(p);
p = p / sum_p;
for i = 2 : c_tv
p(i) = p(i) + p(i-1);
end
e=zeros(n,n);
for i=1:n
for j=1:n
e(i,j)=(c(i)-a(j))^2;
end
end
l=1;
for i=1:n
[f,g]=min(e(l,:));
C(i,:)=[i,g];
l=l+1;
end
e1=zeros(k1,j1);
for i=1:k1
for j=1:j1
e1(i,j)=(a1(i)-c1(j))^2;
end
end
for i=1:k1
for j=1:j1
e1_pro(i,j)=1-e1(i,j)/sum(e1(:,j));%%%%%
%E=zeros(n,2);
%cost=zeros(1,n);
%C=zeros(n,2);
[a1,b1]=sort(measurevalue);
[c1,d1]=sort(pr(b1));
j1=max(size(d1));
if k1~=j1