LOOM, an algorithm for finding local optima of expensive functions

Jeremy Riviere 1 Rodolphe Le Riche 2 Gauthier Picard 1
2 DEMO
LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes, DEMO-ENSMSE - Département Décision en Entreprise : Modélisation, Optimisation
Abstract : Engineering optimization often involves one or many computationally intensive softwares that must be called to calculate the performance of candidate solutions. Despite the calculation cost, it is useful to characterize the global and the local optima. A new algorithm is described here that searches for all the local optima in a reduced number of calls to the true performance functions. The algorithm is based on repeated local searches on metamodels of the true performance functions and called LOOM (LOcal Optima through Metamodels). The local optima are identified as an output of the search. The search distributes computational resources equally among the basins of attraction. This article presents the algorithm and describes a first series of tests in two dimensions where a kriging metamodel is used.
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https://hal-emse.ccsd.cnrs.fr/emse-00867493
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Submitted on : Monday, September 30, 2013 - 9:35:11 AM
Last modification on : Tuesday, October 23, 2018 - 2:36:11 PM

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  • HAL Id : emse-00867493, version 1

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Jeremy Riviere, Rodolphe Le Riche, Gauthier Picard. LOOM, an algorithm for finding local optima of expensive functions. NICST'2013, New and smart Information Communication Science and Technology to support Sustainable Development, Sep 2013, Clermont Ferrand, France. 7 p. ⟨emse-00867493⟩

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