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Towards efficient multiobjective optimization: multiobjective statistical criterions

Ivo Couckuyt UGent, Dirk Deschrijver UGent and Tom Dhaene UGent (2012) IEEE Congress on Evolutionary Computation.
abstract
The use of Surrogate Based Optimization (SBO) is widely spread in engineering design to reduce the number of computational expensive simulations. However, "real-world" problems often consist of multiple, conflicting objectives leading to a set of equivalent solutions (the Pareto front). The objectives are often aggregated into a single cost function to reduce the computational cost, though a better approach is to use multiobjective optimization methods to directly identify a set of Pareto-optimal solutions, which can be used by the designer to make more efficient design decisions (instead of making those decisions upfront). Most of the work in multiobjective optimization is focused on MultiObjective Evolutionary Algorithms (MOEAs). While MOEAs are well-suited to handle large, intractable design spaces, they typically require thousands of expensive simulations, which is prohibitively expensive for the problems under study. Therefore, the use of surrogate models in multiobjective optimization, denoted as MultiObjective Surrogate-Based Optimization (MOSBO), may prove to be even more worthwhile than SBO methods to expedite the optimization process. In this paper, the authors propose the Efficient Multiobjective Optimization (EMO) algorithm which uses Kriging models and multiobjective versions of the expected improvement and probability of improvement criterions to identify the Pareto front with a minimal number of expensive simulations. The EMO algorithm is applied on multiple standard benchmark problems and compared against the well-known NSGA-II and SPEA2 multiobjective optimization methods with promising results.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
GLOBAL OPTIMIZATION, DESIGN OPTIMIZATION, probability of improvement, expected improvement, multiobjective optimization, IBCN, Kriging, ALGORITHM, HYPERVOLUME
in
IEEE Congress on Evolutionary Computation
issue title
2012 IEEE Congress on evolutionary computation (CEC)
pages
7 pages
publisher
IEEE
place of publication
New York, NY, USA
conference name
IEEE Congress on Evolutionary Computation (CEC)
conference location
Brisbane, Australia
conference start
2012-06-10
conference end
2012-06-15
Web of Science type
Proceedings Paper
Web of Science id
000312859303018
ISBN
9781467315098
DOI
10.1109/CEC.2012.6256586
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2985338
handle
http://hdl.handle.net/1854/LU-2985338
date created
2012-09-12 11:15:59
date last changed
2015-08-03 11:28:18
@inproceedings{2985338,
  abstract     = {The use of Surrogate Based Optimization (SBO) is widely spread in engineering design to reduce the number of computational expensive simulations. However, {\textacutedbl}real-world{\textacutedbl} problems often consist of multiple, conflicting objectives leading to a set of equivalent solutions (the Pareto front). The objectives are often aggregated into a single cost function to reduce the computational cost, though a better approach is to use multiobjective optimization methods to directly identify a set of Pareto-optimal solutions, which can be used by the designer to make more efficient design decisions (instead of making those decisions upfront). Most of the work in multiobjective optimization is focused on MultiObjective Evolutionary Algorithms (MOEAs). While MOEAs are well-suited to handle large, intractable design spaces, they typically require thousands of expensive simulations, which is prohibitively expensive for the problems under study. Therefore, the use of surrogate models in multiobjective optimization, denoted as MultiObjective Surrogate-Based Optimization (MOSBO), may prove to be even more worthwhile than SBO methods to expedite the optimization process. In this paper, the authors propose the Efficient Multiobjective Optimization (EMO) algorithm which uses Kriging models and multiobjective versions of the expected improvement and probability of improvement criterions to identify the Pareto front with a minimal number of expensive simulations. The EMO algorithm is applied on multiple standard benchmark problems and compared against the well-known NSGA-II and SPEA2 multiobjective optimization methods with promising results.},
  author       = {Couckuyt, Ivo and Deschrijver, Dirk and Dhaene, Tom},
  booktitle    = {IEEE Congress on Evolutionary Computation},
  isbn         = {9781467315098},
  keyword      = {GLOBAL OPTIMIZATION,DESIGN OPTIMIZATION,probability of improvement,expected improvement,multiobjective optimization,IBCN,Kriging,ALGORITHM,HYPERVOLUME},
  language     = {eng},
  location     = {Brisbane, Australia},
  pages        = {7},
  publisher    = {IEEE},
  title        = {Towards efficient multiobjective optimization: multiobjective statistical criterions},
  url          = {http://dx.doi.org/10.1109/CEC.2012.6256586},
  year         = {2012},
}

Chicago
Couckuyt, Ivo, Dirk Deschrijver, and Tom Dhaene. 2012. “Towards Efficient Multiobjective Optimization: Multiobjective Statistical Criterions.” In IEEE Congress on Evolutionary Computation. New York, NY, USA: IEEE.
APA
Couckuyt, I., Deschrijver, D., & Dhaene, T. (2012). Towards efficient multiobjective optimization: multiobjective statistical criterions. IEEE Congress on Evolutionary Computation. Presented at the IEEE Congress on Evolutionary Computation (CEC), New York, NY, USA: IEEE.
Vancouver
1.
Couckuyt I, Deschrijver D, Dhaene T. Towards efficient multiobjective optimization: multiobjective statistical criterions. IEEE Congress on Evolutionary Computation. New York, NY, USA: IEEE; 2012.
MLA
Couckuyt, Ivo, Dirk Deschrijver, and Tom Dhaene. “Towards Efficient Multiobjective Optimization: Multiobjective Statistical Criterions.” IEEE Congress on Evolutionary Computation. New York, NY, USA: IEEE, 2012. Print.