NDLI: Data driven surrogate-based optimization in the problem solving environment WBCSim

Content Provider	Springer Nature Link
Author	Deshpande, S. Watson, L. T. Shu, J. Kamke, F. A. Ramakrishnan, N.
Copyright Year	2010
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.
Starting Page	211
Ending Page	223
Page Count	13
File Format	PDF
ISSN	01770667
Journal	Engineering with Computers
Volume Number	27
Issue Number	3
e-ISSN	14355663
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-07-14
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Problem solving environment Wood-based composite materials Visualization Optimization Surrogate Response surface approximation Sequential approximate optimization Experiment management Trust region strategy Computer-Aided Engineering (CAD, CAE) and Design Math. Applications in Chemistry Systems Theory, Control Mechanics Calculus of Variations and Optimal Control; Optimization ApplicationMathematics/Computational Methods of Engineering
Content Type	Text
Resource Type	Article
Subject	Engineering Computer Science Applications Modeling and Simulation Software

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