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Recherche transversale
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2020-06-02 - Article/Dans un journal avec peer-review - Anglais - 27 page(s)

Briffoteaux Guillaume , Gobert Maxime , Ragonnet Romain, Gmys Jan , Mezmaz Mohand , Melab Nouredine, Tuyttens Daniel , "Parallel Surrogate-assisted Optimization: Batched Bayesian Neural Network-assisted GA versus q-EGO" in Swarm and Evolutionary Computation

  • Edition : Elsevier, Amsterdam (Netherlands)
  • Codes CREF : Intelligence artificielle (DI1180), Programmation et méthodes de simulation (DI4317), Recherche opérationnelle (DI1150)
  • Unités de recherche UMONS : Mathématique et Recherche opérationnelle (F151)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)
Texte intégral :

Abstract(s) :

(Anglais) Surrogate-based optimization is widely used to deal with long-running black-box simulation-based objective functions. Actually, the use of a surrogate model such as Kriging or Artificial Neural Network allows to reduce the number of calls to the CPU time-intensive simulator. Bayesian optimization uses the ability of surrogates to provide useful information to help guiding effectively the optimization process. In this paper, the Efficient Global Optimization (EGO) reference framework is challenged by a Bayesian Neural Network-assisted Genetic Algorithm, namely BNN-GA. The Bayesian Neural Network (BNN) surrogate is chosen for its ability to provide an uncertainty measure of the prediction that allows to compute the Expected Improvement of a candidate solution in order to improve the exploration of the objective space. BNN is also more reliable than Kriging models for high-dimensional problems and faster to set up thanks to its incremental training. In addition, we propose a batch-based approach for the parallelization of BNN-GA that is challenged by a parallel version of EGO, called q-EGO. Parallel computing is a highly important complementary way (to surrogates) to deal with the computational burden of simulation-based optimization. The comparison of the two parallel approaches is experimentally performed through several benchmark functions and two real-world problems within the scope of Tuberculosis Transmission Control (TBTC). The study presented in this paper proves that parallel batched BNN-GA is a viable alternative to q-EGO approaches being more suitable for high-dimensional problems, parallelization impact, bigger data-bases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.

Mots-clés :
  • (Anglais) Efficient Global Optimization
  • (Anglais) Massively Parallel Computing
  • (Anglais) Surrogate-assisted Optimization
  • (Anglais) Simulation
  • (Anglais) Evolutionary Algorithm
  • (Anglais) Bayesian Optimization