[SciPy-Dev] Global Optimization Benchmarks

[Stefan Endres]

Dear Andrea,

SHGO does not use an initial starting point, only the bounds (which may
also be specified as none or infinite). The benchmarks that I ran used for
the publication used the global minimum as a stopping criteria (together
with performance profiles that demonstrate the final results). For this
particular benchmarking framework I would propose simply using a single
iteration ((dim)^2 +1 points) or specifying 100 starting points.

A script to use 100 sampling points in a single iteration with the sobol
sampling method:

``` result = shgo(obj_fun, bounds, n=100, sampling_method='sobol') ```


If you would like to add a more stochastic element to this performance I
think the best approach would be to use a different seed for the sampling
method (in my experience this does not make much of a difference to the
performance in low dimensional problems), otherwise run shgo only once
and/or with increasing numbers of iterations. Another possibility is to add
a stochastic element to the bounds.

Please let me know if you need any help.

Best regards,
Stefan Endres




On Sun, Jul 26, 2020 at 4:06 PM Andrea Gavana <andrea.gavana at gmail.com>
wrote:

> Dear SciPy developers & users,
>
>      I have a couple of new derivative-free, global optimization
> algorithms I’ve been working on lately - plus some improvements to AMPGO
> and a few more benchmark functions - and I’d like to rerun the benchmarks
> as I did back in 2013 (!!!).
>
> In doing so, I’d like to remove some of the least interesting/worst
> performing algorithms (Firefly, MLSL, Galileo, the original DE) and replace
> them with the ones currently available in SciPy - differential_evolution,
> SHGO and dual_annealing.
>
> Everything seems good and dandy, but it appears to me that SHGO does not
> accept an initial point for the optimization process - which makes the
> whole “run the optimization from 100 different starting points for each
> benchmark” a bit moot.
>
> I am no expert on SHGO, so maybe there is an alternative way to “simulate”
> the changing of the starting point for the optimization? Or maybe some
> other approach to make it consistent across optimizers?
>
> Any suggestion is more than welcome.
>
> Andrea.
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-- 
Stefan Endres (MEng, AMIChemE, BEng (Hons) Chemical Engineering)

Wissenchaftlicher Mitarbetier: Leibniz Institute for Materials Engineering
IWT, Badgasteiner Straße 3, 28359 Bremen, Germany
Work phone (DE): +49 (0) 421 218 51238
Cellphone (DE): +49 (0) 160 949 86417
Cellphone (ZA): +27 (0) 82 972 42 89
E-mail (work): s.endres at iwt.uni-bremen.de
Website: https://stefan-endres.github.io/
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