This paper proposes a novel multi-objective collaborative optimization (MOCO) approach based on multi-objective evolutionary algorithms for complex systems with multiple disciplines and objectives, especially for those systems in which most of the disciplinary variables are shared. The shared variables will conflict when the disciplinary optimizers are implemented concurrently. In order to avoid the confliction, the shared variables are treated as fixed parameters at the discipline level in most of the MOCO approaches. But in this paper, a coordinator is introduced to handle the confliction, which allocates more design freedom and independence to the disciplinary optimizers. A numerical example is solved, and the results are discussed.

References

References
1.
Balling
,
R. J.
, and
Sobieski
,
J.
, 1996, “
Optimization of Coupled Systems: A Critical Overview of Approaches
,”
AIAA J.
,
34
(
1
), pp.
6
17
.
2.
Sobieski
,
J.
, and
Haftka
,
R. T.
, 1997, “
Multidisciplinary Aerospace Design Optimization: Survey of Recent Developments
,”
Struct. Optim.
,
14
(
1
), pp.
1
23
.
3.
de Weck
,
O.
,
Agte
,
J.
,
Sobieski
,
J.
,
Arendsen
,
P.
,
Morris
,
A.
, and
Spieck
,
M.
, 2007, “
State-of-the-Art and Future Trends in Multidisciplinary Design Optimization
,”
48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
, Honolulu, Hawaii.
4.
Sobieski
,
J.
,
Optimization by Decomposition: A Step From Hierarchic to Non-Hierarchic Systems
(
National Aeronautics and Space Administration, Langley Research Center
,
Hampton, VA
, 1988).
5.
Kroo
,
I.
,
Altus
,
S.
,
Braun
,
R.
,
Gage
,
P.
, and
Sobieski
, 1994, “
Multidisciplinary Optimization Methods for Aircraft Preliminary Design
,”
5th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization
, Panama City Beach, FL.
6.
Sobieski
,
J.
,
Agte
,
J. S.
, and
Sandusky
,
R. R.
, 2000, “
Bilevel Integrated System Synthesis
,”
AIAA J.
,
38
(
1
), pp.
164
172
.
7.
Kim
,
H. M.
,
Michelena
,
N. F.
,
Papalambros
,
P. Y.
, and
Jiang
,
T.
, 2003, “
Target Cascading in Optimal System Design
,”
ASME J. Mech. Des.
,
125
(
3
), pp.
474
480
.
8.
Tappeta
,
R. V.
, and
Renaud
,
J. E.
, 1997, “
Multiobjective Collaborative Optimization
,”
ASME J. Mech. Des.
,
119
(
3
), pp.
403
411
.
9.
Deb
,
K.
, 2005, “
Chapter 10: Multi-Objective Optimization
,”
Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques
,
E. K.
Burke
and
G.
Kendall
, eds.,
Springer
,
Berlin/Heidelberg
, pp.
273
316
.
10.
Depince
,
P.
,
Rabeau
,
S.
, and
Bennis
,
F.
, 2005, “
Collaborative Optimization Strategy for Multi-Objective Design
,”
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
, Long Beach, CA.
11.
Aute
,
V.
, and
Azarm
,
S.
, 2006, “
A Genetic Algorithms Based Approach for Multidisciplinary Multiobjective Collaborative Optimization
,”
11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
, Portsmouth, VA.
12.
Li
,
M.
, and
Azarm
,
S.
, 2008, “
Multiobjective Collaborative Robust Optimization With Interval Uncertainty and Interdisciplinary Uncertainty Propagation
,”
ASME J. Mech. Des.
,
130
(
8
),
081402
.
13.
Deb
,
K.
,
Agrawal
,
S.
,
Pratap
,
A.
, and
Meyarivan
,
T.
, 2000, “
A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II
,”
Parallel Problem Solving From Nature PPSN VI
,
M.
Schoenauer
,
K.
Deb
,
G.
Rudolph
,
X.
Yao
,
E.
Lutton
,
J. J.
Merelo
, and
H. P.
Schwefel
, eds.,
Springer
,
Berlin/Heidelberg
, pp.
849
858
.
You do not currently have access to this content.