Efficient Parametric Optimization for Expensive Single Objective Problems

Author:

Weaver-Rosen Jonathan M.1,Malak Richard J.1

Affiliation:

1. Design Systems Laboratory, Mechanical Engineering, Texas A&M University, College Station, TX 77843

Abstract

Abstract Parametric optimization solves optimization problems as a function of uncontrollable or unknown parameters. Such an approach allows an engineer to gather more information than traditional optimization procedures during design. Existing methods for parametric optimization of computationally or monetarily expensive functions can be too time-consuming or impractical to solve. Therefore, new methods for the parametric optimization of expensive functions need to be explored. This work proposes a novel algorithm that leverages the advantages of two existing optimization algorithms. This new algorithm is called the efficient parametric optimization (EPO) algorithm. EPO enables adaptive sampling of a high-fidelity design space using an inexpensive low-fidelity response surface model. Such an approach largely reduces the required number of expensive high-fidelity computations. The proposed method is benchmarked using analytic test problems and used to evaluate a case study requiring finite element analysis. Results show that EPO performs as well as or better than the existing alternative, Predictive Parameterized Pareto Genetic Algorithm (P3GA), for these problems given an allowable number of function evaluations.

Funder

National Aeronautics and Space Administration

Publisher

ASME International

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials

Reference42 articles.

1. Sensitivity Analysis for Nonlinear Programming Using Penalty Methods;Fiacco;Math. Program.,1976

2. Multi-Parametric Programming

3. An Algorithm for Approximate Multiparametric Linear Programming;Filippi;J. Optim. Theory Appl.,2004

4. On Multi-parametric Nonlinear Programming and Explicit Nonlinear Model Predictive Control;Johansen,2002

5. On-line Optimization Via Off-Line Parametric Optimization Tools;Pistikopoulos;Comput. Chem. Eng.,2004

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