Surrogate-based global optimization with parallel simulations

[Felipe A. C. Viana]

Dear all,

Here it is a reference on efficient global optimization (EGO)
algorithm with parallel simulations:

F.A.C. Viana and R.T. Haftka, "Surrogate-based optimization with
parallel simulations using the probability of improvement," 13th AIAA/
ISSMO Multidisciplinary Analysis and Optimization Conference, Fort
Worth, USA, September 13-15, 2010. AIAA 2010-9392.

In surrogate-based optimization, each cycle consists of carrying out a
number of simulations, fitting a surrogate, performing optimization
based on the surrogate, and finally running exact simulation at the
candidate solutions. Adaptive sampling algorithms that add one point
per cycle are readily available. For example, the efficient global
optimization (EGO) algorithm uses the kriging prediction and
uncertainty estimate to guide the selection of the next sampling
point. However, the addition of one point at a time may not be
efficient when it is possible to run simulations in parallel.
Additionally, the extension to include multiple points per cycle turns
out to be either limited or computationally challenging. We propose
an algorithm for adding several points per optimization cycle based on
approximated computation of the probability of improvement. We assume
that the probabilities at different points of the design space are
independent from each other. The approach was tested on three analytic
examples. For these examples we compare our approach with traditional
sequential optimization based on kriging. We found that indeed our
approach was able to deliver better results in a fraction of the
optimization cycles needed by the traditional kriging implementation.

You can find more about it on:
http://sites.google.com/site/fchegury/publications

All the best,
Felipe A. C. Viana