Directed Multiobjective Optimization Based ...
Document type :
Compte-rendu et recension critique d'ouvrage
DOI :
Title :
Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator
Author(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]
Journal title :
Journal of Multi-Criteria Decision Analysis
Special Issue: Evolutionary Multiobjective Optimization: Methodologies and Applications
Special Issue: Evolutionary Multiobjective Optimization: Methodologies and Applications
Pages :
291-317
Publisher :
Wiley
Publication date :
2013-11-04
ISSN :
1057-9214
English keyword(s) :
multiobjective optimization
evolutionary algorithm
hypervolume
preference-based search
evolutionary algorithm
hypervolume
preference-based search
HAL domain(s) :
Informatique [cs]/Réseau de neurones [cs.NE]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
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