Projets par an
Résumé
Many research efforts have been recently focus to solve large-scale global optimization (LSGO) problems by means of evolutionary algorithms. Cooperative co-evolution has been proposed to solve such problems depending on thousands of variables. This methodology has proved very efficient in solving a wide range of LSGO problems. Nevertheless, it often requires an extremely large number of function evaluations to reach a suitable solution. This is somewhat problematic when the function evaluation is computationally expensive. A globally effective approach to high-fidelity optimization problems based on such expensive analyses lies in the exploitation of surrogate models. They act as cheap-to-evaluate alternatives to the original high-fidelity models reducing the computational cost, while still providing improved designs. This kind of optimization process, referred to as surrogate-assisted optimization, has proved very efficient on small-dimensional problems but suffers from the curse of dimensionality to solve LSGO problems. In this paper, cooperative co-evolution was combined with surrogate-assisted optimization in order to efficiently solve high dimensional, expensive and black-box problems. Experimental results are provided on a wide set of benchmark problems and show promising results for the proposed algorithm.
langue originale | Anglais |
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titre | EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization |
rédacteurs en chef | H. C. Rodrigues, J. Herskovits, C. M. Mota Soares, A. L. Araújo, J. M. Guedes, J. O. Folgado, F. Moleiro, J. F. A. Madeira |
Editeur | Springer |
Pages | 41-52 |
Nombre de pages | 12 |
ISBN (Electronique) | 978-3-319-97773-7 |
ISBN (imprimé) | 978-3-030-07401-2, 978-3-319-97772-0 |
Les DOIs | |
Etat de la publication | Publié - 14 sept. 2018 |
Evénement | EngOpt2018: 6th International Conference on Engineering Optimization - Instituto Superior Tecnico, Libsonne, Portugal Durée: 17 sept. 2018 → 19 févr. 2019 http://engopt2018.tecnico.ulisboa.pt/ |
Une conférence
Une conférence | EngOpt2018 |
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Pays/Territoire | Portugal |
La ville | Libsonne |
période | 17/09/18 → 19/02/19 |
Adresse Internet |
mots-clés
- Global optimization
- Surrogate-assisted optimization
- Large-scale optimization
- High dimensional
- Expensive and black-box functions
- Cooperative co-evolutionary algorithm
- Random grouping
- Genetic algorithm
- Evolutionary algorithm
Empreinte digitale
Examiner les sujets de recherche de « A Surrogate-Assisted Cooperative Co-evolutionary Algorithm for Solving High Dimensional, Expensive and Black Box Optimization Problems ». Ensemble, ils forment une empreinte digitale unique.Projets
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Champagne, B., Lazzaroni, R., Geuzaine , C., Chatelain, P. & Knaepen, B.
1/01/18 → 31/12/22
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Thèses de l'étudiant
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Challenging High Dimensionality in Evolutionary Optimization using Cooperative Co-evolutionary Algorithms
Auteur: Blanchard, J., 29 juin 2021Superviseur: Carletti, T. (Promoteur), SARTENAER, A. (Jury), BEAUTHIER, C. (Jury), Mayer, A. (Jury), Tuyttens, D. (Personne externe) (Jury) & El-Abd, M. (Personne externe) (Jury)
Student thesis: Doc types › Docteur en Sciences
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