What is the R equivalent if the scipy.optimize.dual_annealing function in Python

Question

I am translating some code from Python to R, and am finding it hard to find the corresponding functions in each. In this particular case, the code I'm having trouble with is:

output = dual_annealing(
                    residuals_totalcases, x0=parameter_list, bounds=bounds_params
                )

From the scipy.optimize.dual_annealing documentation, I see that the function implements the Dual Annealing optimization.

What packages / functions in R would be equivalent to this?

From the R documentation I see that there's options with the anneal function in the subselect package , as well as GenSA function in the GenSA package, as well as some others.

These all seem to use simulated annealing, but no one discusses dual annealing, which python docs describe as " This stochastic approach derived from the generalization of CSA (Classical Simulated Annealing) and FSA (Fast Simulated Annealing) coupled to a strategy for applying a local search on accepted locations. "

Can anyone offer any clarification? TIA

Answer 1

It is this package: GenSA: Generalized Simulated Annealing, located at

https://cran.r-project.org/web/packages/GenSA/index.html , with the accompanying paper:

Y. Xiang, S. Gubian. B. Suomela, J. Hoeng (2013). Generalized Simulated Annealing for Efficient Global Optimization: the GenSA Package for R. The R Journal, Volume 5/1, June 2013

The scipy documentation references this model as reference #5, saying that they are alternative implementations of each other.