Multi-objective Optimization in CFD by Genetic Algorithm

  • Macro N
  • Desideri J
  • Lanteri S
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{T}his report approaches the question of multi-objective optimization
for optimum shape design in aerodynamics. {T}he employed optimizer
is a semi-stochas- tic method, more precisely a {G}enetic {A}lgorithm
({GA}). {GA}s are very robust optimization algorithms particularly
well suited for problems in which (1) the initialization is not intuitive,
(2) the parameters to be optimized are not all of the same type (boolean,
integer, real, functionnal), (3) the cost functional may present
several local minima, (4) several criteria should be accounted for
simultaneously (multiphysics, efficiency, cost, quality, ...). {I}n
a multi-objective optimization problem, there is no unique optimal
solution but a whole set of potential solutions since in general
no solution is optimal w.r.t. all criteria simultaneously�; instead,
one identifies a set of non-dominated solutions, referred to as the
{P}areto optimal front. {A}fter making these concepts precise, genetic
algorithms are implemented and first tested on academic examples�;
then a numerical experimentation is conducted to solve a multi-objective
shape optimization problem for the design of an airfoil in {E}ulerian

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  • N. Macro

  • J. a. Desideri

  • S. Lanteri

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