Design Space Reduction for Multi-Objective Optimization and Robust Design Optimization Problems Dept. of Mechanical and Industrial Engineering, The University of Manitoba

Format:

Conference/Event

Author/Creator:

Wang, G. Gary, author.

Conference Name:

SAE 2004 World Congress & Exhibition (2004-03-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2004

Summary:

Modern engineering design often involves computation-intensive simulation processes and multiple objectives. Engineers prefer an efficient optimization method that can provide them insights into the problem, yield multiple good or optimal design solutions, and assist decision-making. This work proposed a rough-set based method that can systematically identify regions (or subspaces) from the original design space for multiple objectives. In the smaller regions, any design solution (point) very likely satisfies multiple design goals. Engineers can pick many design solutions from or continue to search in those regions. Robust design optimization (RDO) problems can be formulated as a bi-objective optimization problem and thus in this work RDO is considered a special case of multi-objective optimization (MOO). Examples show that the regions can be efficiently identified. Pareto-optimal frontiers generated from the regions are identical with those generated from the original design space, which indicates that important design information can be captured by only these regions (subspaces). Advantages and limitations of the proposed method are discussed

Notes:

Vendor supplied data

Publisher Number:

2004-01-0240

Access Restriction:

Restricted for use by site license