Multi variable Simulated Annealing with different bounds

Hi there, I have this function that has two variables x and y

fun = @(x,y) x+y-5;

I would like to find the global minimum of this function using SA optimiser. Now the problem that I have here is that I want to use different boundary conditions for x and y like so

x0 = rand;
LBx = 0; % LBx - lower bound for x
UBx= 10; % UBx - upper bound for x
y0 = rand;
LBy = -2; % LB - lower bound for x
UBy= 3; % UB - upper bound for y

The line below is obviously not working but I am posting it as a reference to explain what I am trying to do

[x,y,fval]=simulannealbnd(fun,x0,LBx,UBx,y0,LBy,UBy); %simulated annealing

Thank you very much in advance for your help

Answer 1

[Alan Weiss]

Global Optimization Toolbox solvers, like Optimization Toolbox™ solvers, require you to put all your variables into one vector. The same with the bounds. See Compute Objective Functions and Bound Constraints.

Alan Weiss

MATLAB mathematical toolbox documentation

[Spyros Polychronopoulos]

I thought that, that was the case. I would be a bit difficult to code that now but I will try. Thank you Alan!

Answer 2

[Spyros Polychronopoulos]

Would you maybe know a way to obtain a matrix with all the values x (for all the iterations) the optimizer tried? Thanks in advance