PyGenSA: An Efficient Global Optimization for Generalized Simulated Annealing

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve the determination of the global minimum of multidimensional functions. Python modules from SciPy and PyPI for the implementation of different stochastic methods (i.e.: pyEvolve, SciPy optimize) have been developed and successfully used in the Python scientific community. Based on Tsallis 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, basinhopping and differential evolution (SciPy) on many standard test functions used in optimization problems shows that PyGenSA is more reliable in general and very efficient in particular for high dimension problems.