Directed Multiobjective Optimization Based ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator
Auteur(s) :
Brockhoff, Dimo [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Bader, Johannes [Auteur]
Computer Engineering and Networks Laboratory [TIK]
Thiele, Lothar [Auteur]
Electrical Engineering Institute - EPFL
Zitzler, Eckart [Auteur]
University of Teacher Education [PHBern]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Bader, Johannes [Auteur]
Computer Engineering and Networks Laboratory [TIK]
Thiele, Lothar [Auteur]
Electrical Engineering Institute - EPFL
Zitzler, Eckart [Auteur]
University of Teacher Education [PHBern]
Titre de la revue :
Journal of Multi-Criteria Decision Analysis
Special Issue: Evolutionary Multiobjective Optimization: Methodologies and Applications
Special Issue: Evolutionary Multiobjective Optimization: Methodologies and Applications
Pagination :
291-317
Éditeur :
Wiley
Date de publication :
2013-11-04
ISSN :
1057-9214
Mot(s)-clé(s) en anglais :
multiobjective optimization
evolutionary algorithm
hypervolume
preference-based search
evolutionary algorithm
hypervolume
preference-based search
Discipline(s) HAL :
Informatique [cs]/Réseau de neurones [cs.NE]
Résumé en anglais : [en]
Recently, there has been a large interest in set-based evolutionary algorithms for multiobjective optimization. They are based on the definition of indicators that characterize the quality of the current population while ...
Lire la suite >Recently, there has been a large interest in set-based evolutionary algorithms for multiobjective optimization. They are based on the definition of indicators that characterize the quality of the current population while being compliant with the concept of Pareto-optimality. It has been shown that the hypervolume indicator, which measures the dominated volume in the objective space, enables the design of efficient search algorithms and, at the same time, opens up opportunities to express user preferences in the search by means of weight functions. The present paper contains the necessary theoretical foundations and corresponding algorithms to (i) select appropriate weight functions, to (ii) transform user preferences into weight functions and to (iii) efficiently evaluate the weighted hypervolume indicator through Monte Carlo sampling. The algorithm W-HypE, which implements the previous concepts, is introduced, and the effectiveness of the search, directed towards the user's preferred solutions, is shown using an extensive set of experiments including the necessary statistical performance assessment.Lire moins >
Lire la suite >Recently, there has been a large interest in set-based evolutionary algorithms for multiobjective optimization. They are based on the definition of indicators that characterize the quality of the current population while being compliant with the concept of Pareto-optimality. It has been shown that the hypervolume indicator, which measures the dominated volume in the objective space, enables the design of efficient search algorithms and, at the same time, opens up opportunities to express user preferences in the search by means of weight functions. The present paper contains the necessary theoretical foundations and corresponding algorithms to (i) select appropriate weight functions, to (ii) transform user preferences into weight functions and to (iii) efficiently evaluate the weighted hypervolume indicator through Monte Carlo sampling. The algorithm W-HypE, which implements the previous concepts, is introduced, and the effectiveness of the search, directed towards the user's preferred solutions, is shown using an extensive set of experiments including the necessary statistical performance assessment.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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