Title page for ETD etd-12012009-105248


Type of Document Master's Thesis
Author Deshpande, Shubhangi
URN etd-12012009-105248
Title Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Watson, Layne T. Committee Chair
Ramakrishnan, Naren Committee Member
Shaffer, Clifford A. Committee Member
Keywords
  • Wood based composite materials
  • Experiment management
  • Trust region strategy
  • Sequential approximate optimization
  • Response surface approximation
  • Surrogate
  • Optimization
  • Visualization
  • Problem solving environment
Date of Defense 2009-11-11
Availability unrestricted
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. 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 the 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 (LHS), and central composite design (CCD) are used to train the surrogates. The biggest concern in using the proposed methodology is the generation of the required database. This thesis proposes a data driven approach where an expensive simulation run is required 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 response surface approximations constructed using design of experiments can be effectively managed by a SAO framework based on a trust region strategy. An interesting result is the significant reduction in the number of simulations for the subsequent runs of the optimization algorithm with a cumulatively growing simulation database.

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