GUÉDAS, Benoît ;DÉPINCÉ, Philippe . Coupling functions treatment in a Bi-Level optimization process. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 54, n.6, p. 413-425, august 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/coupling-functions-treatment-in-a-bi-level-optimization-process/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/.
Guédas, B., & Dépincé, P. (2008). Coupling functions treatment in a Bi-Level optimization process. Strojniški vestnik - Journal of Mechanical Engineering, 54(6), 413-425. doi:http://dx.doi.org/
@article{.,
author = {Benoît Guédas and Philippe Dépincé},
title = {Coupling functions treatment in a Bi-Level optimization process},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {54},
number = {6},
year = {2008},
keywords = {multidisciplinary optimization; multi-objective optimization; genetic algorithms; coupled problems; },
abstract = {The optimization of complex engineering systems is often a mix of multi-objective optimization process - each service, discipline has to fulfillseveral objectives - and multidisciplinary optimization process - several disciplines are required. The disciplines are bound to each otherČ outputs of one discipline are used as input of other disciplines (the coupled variables) and a discipline can not have a direct access and knowledge to the whole set of variables. An approximation of the coupled variables is thus needed. The Collaborative Optimization Strategy for Multi-Objective Systems (COSMOS) has been developed at IRCCyN to perform simultaneously multi-objective and multidisciplinary optimization while discipline autonomy is guaranteed. It uses a simple method for the approximation of coupled variables and assumes that the quality of the approximation will increase as the algorithm converges to optimum solutions. In this paper, experiments are made to verify wether this assumption is true. We show that satisfying resultsare found on some test problems but limits of the methods are pointed out.},
issn = {0039-2480}, pages = {413-425}, doi = {},
url = {https://www.sv-jme.eu/sl/article/coupling-functions-treatment-in-a-bi-level-optimization-process/}
}
Guédas, B.,Dépincé, P. 2008 August 54. Coupling functions treatment in a Bi-Level optimization process. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 54:6
%A Guédas, Benoît %A Dépincé, Philippe %D 2008 %T Coupling functions treatment in a Bi-Level optimization process %B 2008 %9 multidisciplinary optimization; multi-objective optimization; genetic algorithms; coupled problems; %! Coupling functions treatment in a Bi-Level optimization process %K multidisciplinary optimization; multi-objective optimization; genetic algorithms; coupled problems; %X The optimization of complex engineering systems is often a mix of multi-objective optimization process - each service, discipline has to fulfillseveral objectives - and multidisciplinary optimization process - several disciplines are required. The disciplines are bound to each otherČ outputs of one discipline are used as input of other disciplines (the coupled variables) and a discipline can not have a direct access and knowledge to the whole set of variables. An approximation of the coupled variables is thus needed. The Collaborative Optimization Strategy for Multi-Objective Systems (COSMOS) has been developed at IRCCyN to perform simultaneously multi-objective and multidisciplinary optimization while discipline autonomy is guaranteed. It uses a simple method for the approximation of coupled variables and assumes that the quality of the approximation will increase as the algorithm converges to optimum solutions. In this paper, experiments are made to verify wether this assumption is true. We show that satisfying resultsare found on some test problems but limits of the methods are pointed out. %U https://www.sv-jme.eu/sl/article/coupling-functions-treatment-in-a-bi-level-optimization-process/ %0 Journal Article %R %& 413 %P 13 %J Strojniški vestnik - Journal of Mechanical Engineering %V 54 %N 6 %@ 0039-2480 %8 2017-08-21 %7 2017-08-21
Guédas, Benoît, & Philippe Dépincé. "Coupling functions treatment in a Bi-Level optimization process." Strojniški vestnik - Journal of Mechanical Engineering [Online], 54.6 (2008): 413-425. Web. 19 Nov. 2024
TY - JOUR AU - Guédas, Benoît AU - Dépincé, Philippe PY - 2008 TI - Coupling functions treatment in a Bi-Level optimization process JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - multidisciplinary optimization; multi-objective optimization; genetic algorithms; coupled problems; N2 - The optimization of complex engineering systems is often a mix of multi-objective optimization process - each service, discipline has to fulfillseveral objectives - and multidisciplinary optimization process - several disciplines are required. The disciplines are bound to each otherČ outputs of one discipline are used as input of other disciplines (the coupled variables) and a discipline can not have a direct access and knowledge to the whole set of variables. An approximation of the coupled variables is thus needed. The Collaborative Optimization Strategy for Multi-Objective Systems (COSMOS) has been developed at IRCCyN to perform simultaneously multi-objective and multidisciplinary optimization while discipline autonomy is guaranteed. It uses a simple method for the approximation of coupled variables and assumes that the quality of the approximation will increase as the algorithm converges to optimum solutions. In this paper, experiments are made to verify wether this assumption is true. We show that satisfying resultsare found on some test problems but limits of the methods are pointed out. UR - https://www.sv-jme.eu/sl/article/coupling-functions-treatment-in-a-bi-level-optimization-process/
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author = {Guédas, B., Dépincé, P.},
title = {Coupling functions treatment in a Bi-Level optimization process},
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volume = {54},
number = {6},
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TY - JOUR AU - Guédas, Benoît AU - Dépincé, Philippe PY - 2017/08/21 TI - Coupling functions treatment in a Bi-Level optimization process JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 54, No 6 (2008): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - multidisciplinary optimization, multi-objective optimization, genetic algorithms, coupled problems, N2 - The optimization of complex engineering systems is often a mix of multi-objective optimization process - each service, discipline has to fulfillseveral objectives - and multidisciplinary optimization process - several disciplines are required. The disciplines are bound to each otherČ outputs of one discipline are used as input of other disciplines (the coupled variables) and a discipline can not have a direct access and knowledge to the whole set of variables. An approximation of the coupled variables is thus needed. The Collaborative Optimization Strategy for Multi-Objective Systems (COSMOS) has been developed at IRCCyN to perform simultaneously multi-objective and multidisciplinary optimization while discipline autonomy is guaranteed. It uses a simple method for the approximation of coupled variables and assumes that the quality of the approximation will increase as the algorithm converges to optimum solutions. In this paper, experiments are made to verify wether this assumption is true. We show that satisfying resultsare found on some test problems but limits of the methods are pointed out. UR - https://www.sv-jme.eu/sl/article/coupling-functions-treatment-in-a-bi-level-optimization-process/
Guédas, Benoît, AND Dépincé, Philippe. "Coupling functions treatment in a Bi-Level optimization process" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 54 Number 6 (21 August 2017)
Strojniški vestnik - Journal of Mechanical Engineering 54(2008)6, 413-425
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The optimization of complex engineering systems is often a mix of multi-objective optimization process - each service, discipline has to fulfillseveral objectives - and multidisciplinary optimization process - several disciplines are required. The disciplines are bound to each otherČ outputs of one discipline are used as input of other disciplines (the coupled variables) and a discipline can not have a direct access and knowledge to the whole set of variables. An approximation of the coupled variables is thus needed. The Collaborative Optimization Strategy for Multi-Objective Systems (COSMOS) has been developed at IRCCyN to perform simultaneously multi-objective and multidisciplinary optimization while discipline autonomy is guaranteed. It uses a simple method for the approximation of coupled variables and assumes that the quality of the approximation will increase as the algorithm converges to optimum solutions. In this paper, experiments are made to verify wether this assumption is true. We show that satisfying resultsare found on some test problems but limits of the methods are pointed out.