research-article

Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm

Authors:

Kaisa Miettinen,

Pekka MakonenAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 1147 - 1155

https://doi.org/10.1145/3321707.3321745

Published: 13 July 2019 Publication History

Abstract

We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multi-objective optimization algorithm. We are motivated by practical applicability and focus on two main challenges faced by practitioners in industry: 1) meaningful formulation of the optimization problem reflecting the needs of a decision maker and 2) finding a desirable solution based on a decision maker's preferences when solving a problem with computationally expensive function evaluations. For the first challenge, we describe the procedure of modelling a component in the air intake ventilation system with commercial simulation tools. The problem to be solved involves time consuming computational fluid dynamics simulations. Therefore, for the second challenge, we extend a recently proposed Kriging-assisted evolutionary algorithm K-RVEA to incorporate a decision maker's preferences. Our numerical results indicate efficiency in using the computing resources available and the solutions obtained reflect the decision maker's preferences well. Actually, two of the solutions dominate the baseline design (the design provided by the decision maker before the optimization process). The decision maker was satisfied with the results and eventually selected one as the final solution.

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Cited By

Yu GWang XJiang CLiu YMa LBo CZhang Q(2024)Transferable preference learning in multi-objective decision analysis and its application to hydrocrackingComplex & Intelligent Systems10.1007/s40747-024-01537-610:5(7401-7418)Online publication date: 15-Jul-2024
https://doi.org/10.1007/s40747-024-01537-6
Aghaei pour PHakanen JMiettinen K(2024)A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problemsJournal of Global Optimization10.1007/s10898-024-01387-z90:2(459-485)Online publication date: 29-May-2024
https://doi.org/10.1007/s10898-024-01387-z
Zhang JHe LIshibuchi H(2023)Dual-Fuzzy-Classifier-Based Evolutionary Algorithm for Expensive Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319566827:6(1575-1589)Online publication date: Dec-2023
https://doi.org/10.1109/TEVC.2022.3195668
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Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm

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cover image ACM Conferences

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

July 2019

1545 pages

ISBN:9781450361118

DOI:10.1145/3321707

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Copyright © 2019 ACM.

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Published: 13 July 2019

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GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Yu GWang XJiang CLiu YMa LBo CZhang Q(2024)Transferable preference learning in multi-objective decision analysis and its application to hydrocrackingComplex & Intelligent Systems10.1007/s40747-024-01537-610:5(7401-7418)Online publication date: 15-Jul-2024
https://doi.org/10.1007/s40747-024-01537-6
Aghaei pour PHakanen JMiettinen K(2024)A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problemsJournal of Global Optimization10.1007/s10898-024-01387-z90:2(459-485)Online publication date: 29-May-2024
https://doi.org/10.1007/s10898-024-01387-z
Zhang JHe LIshibuchi H(2023)Dual-Fuzzy-Classifier-Based Evolutionary Algorithm for Expensive Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319566827:6(1575-1589)Online publication date: Dec-2023
https://doi.org/10.1109/TEVC.2022.3195668
Burkotová JAghaei Pour PKrátký TMiettinen K(2023)Interactive multiobjective optimization of an extremely computationally expensive pump design problemEngineering Optimization10.1080/0305215X.2023.224736956:8(1318-1333)Online publication date: 8-Sep-2023
https://doi.org/10.1080/0305215X.2023.2247369
Deb KFleming PJin YMiettinen KReed P(2023)Key Issues in Real-World Applications of Many-Objective Optimisation and Decision AnalysisMany-Criteria Optimization and Decision Analysis10.1007/978-3-031-25263-1_2(29-57)Online publication date: 29-Jul-2023
https://doi.org/10.1007/978-3-031-25263-1_2
Ji JWong M(2022)Surrogate-assisted Parameter Re-initialization for Differential Evolution2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022088(1592-1599)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022088
Cao CLu XLi YZhu JTang K(2021)The Performance Effect of Model Accuracy on Classification-Assisted Evolutionary Algorithms2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504809(1527-1536)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9504809
Li JZhan ZWang CJin HZhang J(2020)Boosting Data-Driven Evolutionary Algorithm With Localized Data GenerationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2020.297974024:5(923-937)Online publication date: Oct-2020
https://doi.org/10.1109/TEVC.2020.2979740

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