I'm trying to solve the TSP problem using ACO.

Hello? I'm studying ACO (Ant colony optimization).

This time I'm trying to solve the TSP problem with ACO.

I apply the acutal data in this code.

but i cant set starting city.

How can I set a starting city?

And one more thing

It originally departed from the departure city in one direction only.

But What should I do to make it start in two directions?

I attach the code i have.

please help me

thank you

%**************************************************************************
%                 Solving TSP Problem Using ACO
% -------------------------------------------------------------------------
% By     : Sutrisno
% Contact: [e-mail]             Last update: May 2, 2011
% -------------------------------------------------------------------------                             
% This program is developed to find shortest path (minimum cost)between
% some cities. 
% 
% There are 4  parts in this program:
% 1.Main program of ACO (myaco.m)
% 2.Function to generate solution (ant_tour.m)
% 3.Function to calculate the cost (distance) (calculate_cost.m)
% 4.Function to update the traces (update_the_trace.m)
%**************************************************************************

%**************************************************************************
%                   The Program Start Here                    
%*------------------------------------------------------------------------
% function myaco(num_of_nodes,num_of_ants, max_iteration)
function ex11()
% inputs
miter=10;
m=10;
n=20;
% parameters
e=.15;            % evaporation coefficient.
alpha=1;          % effect of ants' sight.
beta=4;           % trace's effect.
t=0.0001*ones(n); % primary tracing.
el=.97;           % common cost elimination. 
% -------------------------------------------------------------------------
% Generate coordinates of cities and plot
%for i=1:n
%    x(i)=rand*20;
%    y(i)=rand*20;
%end    
x=[0.375  0.16  0.19  0.21  0.32  0.375  0.435  0.56  0.645  0.545  0.705  0.745  0.79  0.845  0.35  0.349  0.26  0.215  0.275  0.19];
y=[0.945  0.25  0.185  0.21  0.24  0.09  0.085  0.19  0.235  0.84  0.225  0.25  0.21  0.19  0.75  0.75  0.69  0.565  0.5  0.51];

figure(1);
subplot(1,2,1);
plot(x,y,'o','MarkerFaceColor','k','MarkerEdgeColor','b','MarkerSize',10);
title('Coordinates of Cities');
xlabel('x  (km)');
ylabel('y  (km)');

% generating distace between cities matrix.
for i=1:n
    for j=1:n
        d(i,j)=sqrt((x(i)-x(j))^2+(y(i)-y(j))^2);
    end
end
d=load('factorya.csv');
% generating sight matrix.
for i=1:n
    for j=1:n
        if d(i,j)==0
            h(i,j)=0;
        else
            h(i,j)=1/d(i,j);
        end
    end
end
h=h
% ------------------------------------------------------------------------
%             Main Algorithm: ACO Meta heuristic procedure
% a.  Probabilistic solution construction biased by
%     pheromone trails, without forward pheromone
%     updating
% b.  Deterministic backward path with loop elimination
%     and with pheromone updating--> update_the_trace
% c.  Evaluation of the quality of the solutions
%     generated and use of the solution quality in
%     determining the quantity of pheromone to deposit-->calculate_cost
% -------------------------------------------------------------------------
for i=1:miter
% Step 1: Forward ants and solution construction

% Generate places for each ant    
for j=1:m
    start_places(j,1)=fix(1+rand*(n-1));
end
% Step 2:probabilistic solution contruction   
    [tour]=ant_tour(start_places,m,n,h,t,alpha,beta);
    tour=horzcat(tour,tour(:,1));

% Step 3: Calculate the cost --> total distace
    [cost,f]=calculate_cost(m,n,d,tour,el);
    [t]=update_the_trace(m,n,t,tour,f,e);
    average_cost(i)=mean(cost);

% Step 4: Determine the best route
    [min_cost(i),best_index]=min(cost);
    besttour(i,:)=tour(best_index,:);
    iteration(i)=i;
end
% -------------------------------------------------------------------------

% Plot Average of tour distance vs Number of Iterations
figure(2);plot(iteration,average_cost);
%subplot(2,3,3);
title('Average of tour distance vs Number of iterations');
xlabel('iteration');
ylabel('distance (km)');

% Plot the best route
[k,l]=min(min_cost);
for i=1:n+1
    X(i)=x(besttour(l,i));
    Y(i)=y(besttour(l,i));
end
figure(1);
subplot(1,2,2);plot(X,Y,'--o',...
                'MarkerEdgeColor','k',...
                'MarkerFaceColor','g',...
                'MarkerSize',10)
xlabel('x (km)');ylabel('y (km)');
title(['minimum cost (total length)= ',num2str(k)]);
end
%**************************************************************************
%                   Ending of Program                          
%**************************************************************************