Session: DAC-14-01-Metamodel-Based Design Optimization

Paper Number: 88567

88567 - An Algorithm for Multi-Objective Efficient Parametric Optimization 

Parametric optimization is the process of solving an optimization problem as a function of currently unknown or changing variables known as parameters. Parametric optimization methods have been shown to benefit engineering design and optimal morphing applications through its specialized problem formulation and specialized algorithms. Despite its benefits to engineering design, existing parametric optimization algorithms (similar to many optimization algorithms) can be inefficient when design analyses are expensive and resources are limited. Since many engineering design problems involve some level of expensive analysis, a more efficient parametric optimization algorithm is needed. Therefore, the multi-objective efficient parametric optimization (MO-EPO) algorithm is developed to allow for the efficient optimization of problems with multiple parameters and objectives. This technique expands the single objective efficient parametric optimization (EPO) algorithm by utilizing the parametric hypervolume indicator (pHVI) which measures the space dominated by a given set of data involving both objectives and parameters. The novel MO-EPO algorithm is demonstrated on three analytical benchmarking problems scaled to various numbers of objectives and parameters. In each case, MO-EPO is shown to find solutions that are as good as or better than those found from the existing P3GA (i.e., equal or greater pHVI value) when the number of design evaluations is limited. 

Presenting Author: Jonathan Weaver-Rosen Texas A&M University

Presenting Author Biography: Jonathan Weaver-Rosen is an Instructional Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research has focused largely on design automation and methodologies. His research on parametric optimization has shown to be beneficial in the design of morphing or otherwise adaptive systems.

Authors:

Jonathan Weaver-Rosen Texas A&M University
Richard Malak Texas A&M University