Approximating Pareto Set Topology by Cubic ...
Type de document :
Communication dans un congrès avec actes
Titre :
Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems
Auteur(s) :
Marca, Yuri [Auteur]
Shinshu University [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Martinez, Saúl Zapotecas [Auteur]
Faculty of Engineering [Nagano]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Shinshu University [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Martinez, Saúl Zapotecas [Auteur]
Faculty of Engineering [Nagano]
Liefooghe, Arnaud [Auteur]

Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]

Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Éditeur(s) ou directeur(s) scientifique(s) :
Kalyanmoy Deb
Erik Goodman
Carlos A. Coello Coello
Kathrin Klamroth
Kaisa Miettinen
Sanaz Mostaghim
Patrick Reed
Erik Goodman
Carlos A. Coello Coello
Kathrin Klamroth
Kaisa Miettinen
Sanaz Mostaghim
Patrick Reed
Titre de la manifestation scientifique :
EMO 2019 - International Conference on Evolutionary Multi-Criterion Optimization
Ville :
East Lansing, Michigan
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2019-03-10
Titre de l’ouvrage :
Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings
Titre de la revue :
Lecture Notes in Computer Science (LNCS)
Date de publication :
2019-02-03
Mot(s)-clé(s) en anglais :
Difficult Pareto set topology
Multi-objective optimization
Interpolation
Evolutionary algorithm
Multi-objective optimization
Interpolation
Evolutionary algorithm
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
Difficult Pareto set topology refers to multi-objective problems with geometries of the Pareto set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These ...
Lire la suite >Difficult Pareto set topology refers to multi-objective problems with geometries of the Pareto set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto set topology. These algorithms approach very differently the Pareto set. We argue and discuss their behavior and its implications for model building.Lire moins >
Lire la suite >Difficult Pareto set topology refers to multi-objective problems with geometries of the Pareto set such that neighboring optimal solutions in objective space differ in several or all variables in decision space. These problems can present a tough challenge for evolutionary multi-objective algorithms to find a good approximation of the optimal Pareto set well-distributed in decision and objective space. One important challenge optimizing these problems is to keep or restore diversity in decision space. In this work, we propose a method that learns a model of the topology of the solutions in the population by performing parametric spline interpolations for all variables in decision space. We use Catmull-Rom parametric curves as they allow us to deal with any dimension in decision space. The proposed method is appropriated for bi-objective problems since their optimal set is a one-dimensional curve according to the Karush-Kuhn-Tucker condition. Here, the proposed method is used to promote restarts from solutions generated by the model. We study the effectiveness of the proposed method coupled to NSGA-II and two variations of MOEA/D on problems with difficult Pareto set topology. These algorithms approach very differently the Pareto set. We argue and discuss their behavior and its implications for model building.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.archives-ouvertes.fr/hal-02064548/document
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-02064548/document
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-02064548/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- emo2019_yuri.pdf
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- emo2019_yuri.pdf
- Accès libre
- Accéder au document