Openmdao 1.x: Efficient way to implement Expected Improvement

1
votes

I am currently using Openmdao 1.7.1. I am trying to have a MetaModel with Kriging train itself at the best point of Expected Improvement. The aim is to find a global optimum on a compact design space with an EGO-like method.

However I am facing the following conundrum:

In order to find the best point, the only way I see is to run an optimization on the Expected Improvement function with a gradient base optimizer in a nested Problem, with an outer problem running a FixedPointIterator, checking on the value of the Expected Improvement value.

My questions are the following:

  • Is there another, more efficient way of doing this ? I couldn't find anything about EGO in Openmdao 1.x, if there is, where should I look ?
  • If this is the only way:
    • Will this find the global optimum in my design space ?

Thank you in advance for your responses.

1
openmdao

1 Answers

1
votes

I think that you could develop EGO as a stand alone driver. The driver would be responsible for running the underlying model, collecting the cases, building the surrogate and doing its own sub-optimization.

You can use the surrogate models built into OpenMDAO for this. You just wouldn't use the meta-model component. You would just use the surrogate model by itself. For an example of how to do that, look at this test which runs kriging by itself.

So 90% of the EGO process would be wrapped up into a driver. This avoids the need for a sub-problem and I think simplifies the code significantly. The EGO algorithm is fairly simple and is not hard to code into the driver. You won't gain much by using nested-problems to implement it. But by making it a driver, you can still build a more complex model that will get run by EGO.