This section presents an example that shows how to find a local minimum of a function using simulated annealing.
De Jong's fifth function is a two-dimensional function with many (25) local minima:
dejong5fcn
Many standard optimization algorithms get stuck in local minima. Because the simulated annealing algorithm performs a wide random search, the chance of being trapped in local minima is decreased.
Note: Because simulated annealing uses random number generators, each time you run this algorithm you can get different results. See Reproduce Your Results for more information. |
To run the simulated annealing algorithm without constraints,
call simulannealbnd
at the command line using
the objective function in dejong5fcn.m
, referenced
by anonymous function pointer:
rng(10,'twister') % for reproducibility fun = @dejong5fcn; [x fval] = simulannealbnd(fun,[0 0])
This returns
Optimization terminated: change in best function value less than options.TolFun. x = -16.1292 -15.8214 fval = 6.9034
where
x
is the final point returned by
the algorithm.
fval
is the objective function
value at the final point.
To run the minimization using the Optimization app,
Set up your problem as pictured in the Optimization app
Click Start under Run solver and view results:
Your results can differ from the pictured
ones, because simulannealbnd
uses a random number
stream.