Differential evolution global optimization in Python.
.. image:: https://zenodo.org/badge/5871/hpparvi/PyDE.svg :target: https://zenodo.org/badge/latestdoi/5871/hpparvi/PyDE
Global optimization using differential evolution in Python [Storn97]_.
::
git clone https://github.com/hpparvi/PyDE.git
cd PyDE
python setup.py install [--user]
Import the class from the package
::
from pyde.de import DiffEvol
Create a DiffEvol instance
::
de = DiffEvol(minfun, bounds, npop)
where minfun
is the function to be optimized, bounds
is an initialization array,
and npop
is the size of the parameter vector population.
Now, you can run the optimizer ngen
generations::
res = de.optimize(ngen=100)
or run the optimizer as a generator::
for res in de(ngen=100):
do something
The PyDE parameter vector population can be used to initialize the affine-invariant MCMC sampler
emcee <http://dan.iel.fm/emcee/current/>
_ when a simple point estimate of the function minimum
(or maximum) is not sufficient::
de = DiffEvol(lnpost, bounds, npop, maximize=True)
de.optimize(ngen)
sampler = emcee.EnsembleSampler(npop, ndim, lnpost)
sampler.run_mcmc(de.population, 1000)
.. [Storn97] Storn, R., Price, K., Journal of Global Optimization 11: 341--359, 1997
pyde.de.\ DiffEvol (minfun
, bounds
, npop
, F=0.5
, C=0.5
,
seed=0
, maximize=False
)
Parameters
:minfun
: Function to be minimized.
:bounds
: Parameter space bounds as [npar,2]
array.
:npop
: Size of the parameter vector population.
:F
: Difference amplification factor. Values between 0.5-0.8 are good in most cases.
:C
: Cross-over probability. Use 0.9 to test for fast convergence, and smaller values (~0.1) for a more elaborate search.
:seed
: Random seed.
:maximize
: An optional switch telling whether we want maximize or minimize the function. Defaults to minimization.