Title :

Approximating Pareto Set Topology by Cubic Interpolation on Bi-objective Problems

Author(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]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]
Derbel, Bilel [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]

Scientific editor(s) :

Kalyanmoy Deb
Erik Goodman
Carlos A. Coello Coello
Kathrin Klamroth
Kaisa Miettinen
Sanaz Mostaghim
Patrick Reed

Conference title :

EMO 2019 - International Conference on Evolutionary Multi-Criterion Optimization

City :

East Lansing, Michigan

Country :

Etats-Unis d'Amérique

Start date of the conference :

2019-03-10

Book title :

Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings

Journal title :

Lecture Notes in Computer Science (LNCS)

Publication date :

2019-02-03

English keyword(s) :

Difficult Pareto set topology
Multi-objective optimization
Interpolation
Evolutionary algorithm

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL