Surrogate-based agents for constrained optimization

Abstract : Multi-agent systems have been used to solve complex problems by decomposing them into autonomous subtasks. Drawing inspiration from both multi-surrogate and multi-agent techniques, we de ne in this article optimization subtasks that employ di erent approxi- mations of the data in subregions through the choice of surrogate, which creates surrogate- based agents. We explore a method of design space partitioning that assigns agents to subregions of the design space, which drives the agents to locate optima through a mixture of optimization and exploration in the subregions. These methods are illustrated on two constrained optimization problems, one with uncertainty and another with small, discon- nected feasible regions. It is observed that using a system of surrogate-based optimization agents is more e ective at locating the optimum compared to optimization with a single surrogate over the entire design space.
Type de document :
Communication dans un congrès
14th AIAA Non-Deterministic Approaches Conference, Apr 2012, Honolulu, United States. AIAA, 2012, 〈10.2514/6.2012-1935〉
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https://hal-emse.ccsd.cnrs.fr/emse-00680732
Contributeur : Florent Breuil <>
Soumis le : mardi 20 mars 2012 - 09:35:17
Dernière modification le : mardi 17 octobre 2017 - 12:08:01

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Diane Villanueva, Rodolphe Le Riche, Gauthier Picard, Raphael Haftka. Surrogate-based agents for constrained optimization. 14th AIAA Non-Deterministic Approaches Conference, Apr 2012, Honolulu, United States. AIAA, 2012, 〈10.2514/6.2012-1935〉. 〈emse-00680732〉

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