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.
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https://hal-emse.ccsd.cnrs.fr/emse-00680732
Contributor : Florent Breuil <>
Submitted on : Tuesday, March 20, 2012 - 9:35:17 AM
Last modification on : Thursday, October 17, 2019 - 12:36:13 PM

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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. ⟨10.2514/6.2012-1935⟩. ⟨emse-00680732⟩

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