Comparison between MGDA and PAES for Multi-Objective Optimization

Adrien Zerbinati 1 Jean-Antoine Desideri 1 Régis 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 : In multi-objective optimization, the knowledge of the Pareto set provides valuable information on the reachable optimal performance. A number of evolutionary strategies (PAES, NSGA-II, etc), have been proposed in the literature and proved to be successful to identify the Pareto set. However, these derivative-free algorithms are very remanding in terms of computational time. Today, in many areas of computational sciences, codes are developed that include the calculation of the gradient, cautiously validated and calibrated. Thus, an alternate method applicable when the gradients are known is introduced here. Using a clever combination of the gradients, a descent direction common to all criteria is identified. As a natural outcome, the Multiple Gradient Descent Algorithm (MGDA) is defined as a generalization of steepest-descent method and compared with PAES by numerical experiments.
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[Research Report] RR-7667, INRIA. 2011, pp.15
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Soumis le : vendredi 1 juillet 2011 - 14:55:42
Dernière modification le : jeudi 3 mai 2018 - 13:32:55
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  • HAL Id : inria-00605423, version 1

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Adrien Zerbinati, Jean-Antoine Desideri, Régis Duvigneau. Comparison between MGDA and PAES for Multi-Objective Optimization. [Research Report] RR-7667, INRIA. 2011, pp.15. 〈inria-00605423〉

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