[SciPy-Dev] optimize - add algorithm for global optimization: GenSA

[Andrew Nelson]

I have a scipy PR for testing global optimisers. It has approx 120
functions, which is 2.5 times more than in the linked paper. I think for
any global optimisers to be added it should give a good performance against
those benchmarks. The main criterion is the number of successes and the avg
number of function evaluations. Time is of secondary importance.
Any optimisers added to scipy.optimize should conform to the generally
standardised syntax (naming conventions, etc) used by the module.
On 24 Oct 2015 1:06 am, "Jacob Stevenson" <jstevenson131 at gmail.com> wrote:

> In my opinion a robust implementation of a simulated annealing based
> optimizer would be welcome.  There are cases when this would be preferable
> to basinhopping, e.g. when non-smooth or non-continuous functions make the
> local optimization step in basinhopping less effective.
>
> I think the first step is to make a pull request (or send a link if you
> already did) where we can review the code and have discussions.
>
> Best,
> Jake
>
> On Fri, 23 Oct 2015 at 14:10 Gubian, Sylvain <Sylvain.Gubian at pmi.com>
> wrote:
>
>> Hi everyone,
>>
>> We would like to propose a new method, GenSA,  for global optimization to
>> be included in the optimize module.
>>
>> GenSA is an implementation of the General Simulated Annealing algorithm
>> (GSA, http://www.sciencedirect.com/science/article/pii/S0378437196002713).
>> This approach generalizes CSA (Classical Simulated Annealing) and FSA (Fast
>> Simulated Annealing) to search for the global minimum more efficiently. The
>> algorithm is explained in more detail in this reference:
>> http://journal.r-project.org/archive/2013-1/xiang-gubian-suomela-etal.pdf
>> .
>>
>> SciPy has already in the past included a method based on simulated
>> annealing, called anneal, which has been deprecated in 0.14 (with an advice
>> to use basinhopping) and eventually removed in 0.16.
>>
>> A previously published comparison of 18 optimization methods in the R
>> language (http://www.jstatsoft.org/v60/i06/paper) shows that GenSA is,
>> among the methods tested, one of the “most capable of consistently
>> returning a solution near the global minimum of each test function”. This
>> paper however did not consider basinhopping, so we have performed some
>> tests which tend to show that GenSA is more efficient than basinhopping
>> for high dimension problems. The results have been presented in a poster in
>> PyCon UK 2015 (Coventry).
>>
>> The code is ready and passes unit tests and PEP8. We hope it would be a
>> useful addition to SciPy and would be happy to have your opinion.
>>
>> Thanks,
>>
>> Sylvain.
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>
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