Meta-model-assisted MGDA for multi-objective functional optimization - Archive ouverte HAL
Journal Articles Computers and Fluids Year : 2014

Meta-model-assisted MGDA for multi-objective functional optimization

Abstract

A novel numerical method for multi-objective differentiable optimization, the Multiple-Gradient Descent Algorithmm (MGDA), has been proposed in [8] [11] to identify Pareto fronts. In MGDA, a direction of search for which the directional gradients of the objective functions are all negative, and often equal by construction [12], is identified and used in a steepest-descent-type iteration. The method converges to Pareto-optimal points. MGDA is here briefly reviewed to outline its principal theoretical properties and applied first to a classical mathematical test-case for illustration. The method is then ex-tended encompass cases where the functional gradients are approximated via meta-models, as it is often the case in complex situations, and demonstrated on three optimum-shape design problems in compressible aerodynamics.The first problem is purely related to aerodynamic performance. It is a wing shape optimization exercise w.r.t. lift and drag in typical transonic cruise
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Dates and versions

hal-01082595 , version 1 (13-11-2014)

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Adrien Zerbinati, Andrea Minelli, Imane Ghazlane, Jean-Antoine Desideri. Meta-model-assisted MGDA for multi-objective functional optimization. Computers and Fluids, 2014, 102 (10), pp.116 - 130. ⟨10.1016/j.compfluid.2014.06.018⟩. ⟨hal-01082595⟩
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