[SciPy-User] Parameter for simulated annealing

[Charles R Harris]

On Mon, Jan 10, 2011 at 3:25 PM, scipy.optimize <
scipy.optimize at googlemail.com> wrote:

> To whom it may concern,
>
> I am trying to find good settings for simulated annealing. But at the
> moment I fail. The fmin-optimizer is working properly but he does not find
> the global minimum. No big surprise.
>
> My problem:
> I have 6 Variables. 4 of them should be between -0.05 and 0.05 and 2 of
> them between -45 and 45. I normalize them so they are all around -0.1 to
> 0.1.
> The target function should be 1. I have implemented constraints by adding a
> 10 to my target function if one of my variables are beyond their range. But
> the simulated annealing does not respect the constraints. Most of my
> iterations it tried to find a value out of my given range.
>
> What I have done:
> I vary 2 parameter: dwell and T0
> dwell from 50 to 1000
> T0 from 0.2 to 1.2
> And this with the 3 different models (anneal_boltzman, anneal_cauchy and
> anneal_fast).
>
> When I have T0=0.2 and dwell=100 the computing range simulated annealing is
> trying is about -0.08 to 0.08. This was my best result. When I raise T0 and
> dwell the range grow. But even after 600 iteration there is not a trend to
> see.
> I have computed nearly 20 variations of these parameter but I could not
> find a big sensitivity.
>
> The variation was basically blind because I have no exactly idea what these
> 2 parameter stands for. I have read the manuals up and down.
>
> Can anyone give me an approximate range in which I should search my perfect
> parameter for dwell and T0? The best thing would be, when I can make a rough
> estimate of my minimum with the simulated annealing and after this I would
> run the fmin-optimizer. Because the biggest problem is that 1 computing (1
> iteration) takes about 1h. So If I can separate the search I can save much
> time. (I have a testmodus in which 1 Iteration takes about 15min.)
>
> I hope someone can help me. Anyway, thanks for your help.
>
>

I also hope someone has a good answer because my experience is that
simulated annealing is rather sensitive to the choice of parameters. When it
works, it's great, but genetic algorithms tend to be more robust.

Chuck
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