Data driven surrogate-based optimization in the problem solving environment WBCSim |
| |
Authors: | S Deshpande L T Watson J Shu F A Kamke N Ramakrishnan |
| |
Affiliation: | (1) Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0106, USA;(2) Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0106, USA;(3) Department of Wood Science and Engineering, Oregon State University, Corvallis, OR 97331, USA |
| |
Abstract: | Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these
problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due
to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally
cheap(er) approximations of the expensive simulations that mimic the behavior of the simulation model as closely as possible.
This paper presents a data driven, surrogate-based optimization algorithm that uses a trust region-based sequential approximate
optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm
is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms
to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling, and central composite
design—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling
an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the
generation of the required database. As the number of design variables grows, the computational cost of generating the required
database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive
simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database
matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically
reduces the total number of calls to the expensive simulation runs during the optimization process. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|