Running gradient-free optimization methods in parallel with OpenMDAO and PyOptSparse

0
votes

I would like to run ALPSO and NSGA2 from OpenMDAO using the PyOptSparse driver in parallel. The catch is that I don't want to run the model itself in parallel (which I have done frequently in OpenMDAO), I just want to run the optimization computations in parallel (e.g. distribute the calculations for swarm members of ALPSO). I have been looking through the documentation and source for all of the above mentioned codes, but I have not found a way to do this. Could someone point me in the right direction?

Note: I am currently using OpenMDAO 1.7.3, but I am open to answers involving later versions

1
optimizationparallel-processingopenmdao

1 Answers

1
votes

I don't believe that those optimizers support parallel execution. It would most likely require modifications to the code in ALPSO/NSGA2, pyoptsparse, and the pyoptsparse driver to support this.

In OpenMDAO 2.2 (the latest version), we do have a simple GA driver that can run the evaluation of points in the population in parallel, so maybe that is an option. (it is pretty simple though, and only supports single objective.)