Multi variable Simulated Annealing with different bounds
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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
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Alan Weiss
2018-9-13
0 个投票
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
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Spyros Polychronopoulos
2018-9-21
编辑：Spyros Polychronopoulos
2018-9-21
0 个投票
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