Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F28645413%3A_____%2F17%3AN0000001" target="_blank" >RIV/28645413:_____/17:N0000001 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/7969486/" target="_blank" >https://ieeexplore.ieee.org/document/7969486/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC.2017.7969486" target="_blank" >10.1109/CEC.2017.7969486</a>

Result language
angličtina
Original language name
Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system
Original language description
We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and involves computational fluid dynamics (CFD) simulations. We describe the modeling of the system and its numerical evaluation with different commercial software to obtain three expensive objective functions. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. From the set of nondominated solutions generated by K-RVEA, a decision maker having substance knowledge selected the final one based on his preferences. The final selected solution has better objective function values compared to the baseline solution of the initial design. A comparison of solutions with K-RVEA and RVEA (which does not use surrogates) is also performed to show the potential of using surrogates.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Evolutionary Computation (CEC), 2017 IEEE Congress on
ISBN
978-1-5090-4602-7
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
IEEE
Place of publication
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Event location
San Sebastian, Spain
Event date
Jun 5, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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