How to use start and target node in ant colony optimization?

I will give 11 nodes as input for Ant colony optimization(ACO) and find a shortest path with connecting all nodes. The output image and code of ACO is attached. Here, i have one doubt. How to give start and target node in this code for find shortest path between start and target node?[In my question, not necessary to connect all the nodes between start and target node. The only aim to find shortest path between start and target node]. For example, start node=(9, 76) and target node=(97, 17).

%%%%%%%%%%%%%%CreateModel%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function model=CreateModel()

      x=[82 91  92 63 9 28 55 96 97  98 96  ];

      y=[66 3  94 68 76 75 39 66 17  3 27  ];

      n=numel(x);

      D=zeros(n,n);

      for i=1:n-1
          for j=i+1:n

              D(i,j)=sqrt((x(i)-x(j))^2+(y(i)-y(j))^2);

              D(j,i)=D(i,j);

          end
      end

      model.n=n;
      model.x=x;
      model.y=y;
      model.D=D;

end

%%%%%%%%%%%%%%%%PlotSolution%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function PlotSolution(tour,model)

      tour=[tour tour(1)];

      plot(model.x(tour),model.y(tour),'k-o',...
          'MarkerSize',10,...
          'MarkerFaceColor','y',...
          'LineWidth',1.5);

      xlabel('x');
      ylabel('y');

      axis equal;
      grid on;

  alpha = 0.1;
  
    xmin = min(model.x);
    xmax = max(model.x);
    dx = xmax - xmin;
    xmin = floor((xmin - alpha*dx)/10)*10;
    xmax = ceil((xmax + alpha*dx)/10)*10;
    xlim([xmin xmax]);

      ymin = min(model.y);
      ymax = max(model.y);
      dy = ymax - ymin;
      ymin = floor((ymin - alpha*dy)/10)*10;
      ymax = ceil((ymax + alpha*dy)/10)*10;
      ylim([ymin ymax]);

end

%%%%%%%%%%%%%%%%RouletteWheelSelection%%%%%%%%%%%%%%%%%%%%%%%
function j=RouletteWheelSelection(P)

      r=rand;

      C=cumsum(P);

      j=find(r<=C,1,'first');

end

%%%%%%%%%%%%%%%%%%%%TourLength%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function L=TourLength(tour,model)

      n=numel(tour);

      tour=[tour tour(1)];

      L=0;
      for i=1:n
          L=L+model.D(tour(i),tour(i+1));
      end

end

%%%%%%%%%%%%%%%%%%%%%%%ACO %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
clear;
close all;

%%Problem Definition

model=CreateModel();

CostFunction=@(tour) TourLength(tour,model);

nVar=model.n;

%%ACO Parameters

MaxIt=30;      % Maximum Number of Iterations

nAnt=40;        % Number of Ants (Population Size)

Q=1;

tau0=10*Q/(nVar*mean(model.D(:)));  % Initial Phromone

alpha=1;        % Phromone Exponential Weight
beta=1;         % Heuristic Exponential Weight

rho=0.05;       % Evaporation Rate

%%Initialization

eta=1./model.D;             % Heuristic Information Matrix

tau=tau0*ones(nVar,nVar);   % Phromone Matrix

BestCost=zeros(MaxIt,1);    % Array to Hold Best Cost Values

% Empty Ant
empty_ant.Tour=[];
empty_ant.Cost=[];

% Ant Colony Matrix
ant=repmat(empty_ant,nAnt,1);

% Best Ant
BestSol.Cost=inf;

%%ACO Main Loop

for it=1:MaxIt

      % Move Ants
      for k=1:nAnt

          ant(k).Tour=randi([1 nVar]);

          for l=2:nVar

              i=ant(k).Tour(end);

              P=tau(i,:).^alpha.*eta(i,:).^beta;

              P(ant(k).Tour)=0;

              P=P/sum(P);

              j=RouletteWheelSelection(P);

              ant(k).Tour=[ant(k).Tour j];

          end

          ant(k).Cost=CostFunction(ant(k).Tour);

          if ant(k).Cost<BestSol.Cost
              BestSol=ant(k);
          end

      end

      % Update Phromones
      for k=1:nAnt

          tour=ant(k).Tour;

          tour=[tour tour(1)]; %#ok

          for l=1:nVar

              i=tour(l);
              j=tour(l+1);

              tau(i,j)=tau(i,j)+Q/ant(k).Cost;

          end

      end

      % Evaporation
      tau=(1-rho)*tau;

      % Store Best Cost
      BestCost(it)=BestSol.Cost;

      % Show Iteration Information
      disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);

      % Plot Solution
      figure(1);
      PlotSolution(BestSol.Tour,model);
      pause(0.01);

end

%%Results

figure;
plot(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;

%

[izzuddin akmal]

hi, can u explain what the function of nVar ?

Answer 1

[REMYA R suresh]

can you explain ,how to fit new function in this coding ?