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Metamodel-assisted particle swarm optimization and application to aerodynamic shape optimization

Praveen Chandrashekarappa 1 Regis Duvigneau 1
1 OPALE - Optimization and control, numerical algorithms and integration of complex multidiscipline systems governed by PDE
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : Modern optimization methods like Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been found to be very robust and general for solving engineering design problems. They require the use of large population size and may suffer from slow convergence. Both of these lead to large number of function evaluations which can significantly increase the cost of the optimization. This is especially so in view of the increasing use of costly high fidelity analysis tools like CFD. Metamodels also known as surrogate models, are a cheaper alternative to costly analysis tools. In this work we construct radial basis function approximations and use them in conjunction with particle swarm optimization in an inexact pre-evaluation procedure for aerodynamic design. We show that the use of mixed evaluations by metamodels/CFD can significantly reduce the computational cost of PSO while yielding optimal designs as good as those obtained with the costly evaluation tool.
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Submitted on : Wednesday, February 27, 2008 - 11:32:57 AM
Last modification on : Friday, January 21, 2022 - 11:28:05 AM
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  • HAL Id : inria-00199773, version 4


Praveen Chandrashekarappa, Regis Duvigneau. Metamodel-assisted particle swarm optimization and application to aerodynamic shape optimization. [Research Report] RR-6397, INRIA. 2007, pp.39. ⟨inria-00199773v4⟩



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