How to create simulated annealing objective function?

Hello!

I want to create a objective function for Travelling salesman problem using simulated annealing with global optimization toolbox.

Does anyone have idea how to make these?

Thank you for help

[Walter Roberson]

See http://uk.mathworks.com/matlabcentral/answers/64840-can-anyone-suggest-me-to-write-a-correct-and-working-code-for-traveling-salesman-problem-using-simul for one approach

You might also find it useful to look at the Traveling Salesman GA demo. I cannot look at it myself as I do not have the appropriate license, but the link is said to be http://www.mathworks.com/products/global-optimization/demos.html?file=/products/demos/shipping/globaloptim/traveling_salesman_demo.html

[jan grlica]

Thank you.

Yes i can't look on the second link too.

[jan grlica]

for now my programe is looking like these: one .m file:

clc

close all

clear all

global pot

global y

global x

global bestfval_log

global N

bestfval_log = [];

ime=[1 2 3];

str1 = 'Enka';

str2 = 'Dvojka';

str3 = 'Trojka';

x=[1 2 3];

y=[1 2 3];

N=length(x);

figure ('Name', 'Popotnik');

axes('Color', [0.5 0.5 0.5]);

axis([0 10 0 10]);

hold on;

for i=1:N

      plot(x(i), y(i), 'g+');
  end

      text(x(1),y(1),str1);
      text(x(2),y(2),str2);
      text(x(3),y(3),str3);
      pot=1:N;
  x0=[0 0];

[x, fval, exitFlag, output] = simulannealbnd(@kriterijskafunckija, x0);

and my objective function is:

function d = kriterijskafunkcija(pot)

%global pot

global x

global y

global N

%global d

d=0;

for i=1:N-1

      d=d+((x(pot(i))-x(pot(i+1)))^2+(y(pot(i))-y(pot(i+1)))^2)^.5;
  end

i=N;

d=d+((x(pot(i))-x(pot(1)))^2+(y(pot(i))-y(pot(1)))^2)^.5;

anyone knows how to move forward and make these work

Answer 1

[Alan Weiss]

The simulated annealing solver is based on continuous variables by default. Perhaps you could make it work for a custom data type, similar to the genetic algorithm solution. But travelling salesman problems are most easily formulated for integer variables. For solutions, see this MILP example which uses Optimization Toolbox, or this genetic algorithm example using Global Optimization Toolbox and a custom data type.

Alan Weiss

MATLAB mathematical toolbox documentation

[jan grlica]

Thank you for help but I just cant make it work...

Answer 2

[Walter Roberson]

See my discussion in http://uk.mathworks.com/matlabcentral/answers/220440-simulated-annealing-how-to-solve-this-error as to what the problem is here (the code has exactly the same basic problem here), and as to why it is hard to fix.

The short summary: you can use Simulated Annealing techniques with Traveling Salesperson, but using simulannealbnd() itself for the problem is going to be difficult.