An Efficient Global Optimization Python Module based on Generalized Simulating Annealing: PyGenSA


Authored by  S Cano, S Gubian, Y Xiang, F Martin

Presented at EuroPython 2016     

Abstract

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve the determination of the global minimum of multi dimensional functions. Python modules from SciPy and PyPI for the implementation of different stochastic methods (i.e.: pyEvolve, SciPyoptimize) have been developed and successfully used in the Python scientific community. Based onTsallis statistics, the PyGenSA python module has been developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. Testing PyGenSA, basin hopping (SciPy) and differential evolution (SciPy) on many standard test functions used in optimization problems shows that PyGenSA is more reliable in general and more efficient in particular for high dimension problems. 

an-efficient-global-optimization-python-module-based-on-generalized-simulating-annealing-pygensa