Titre :

A fitness landscape analysis of Pareto local search on bi-objective permutation flowshop scheduling problems

Auteur(s) :

Liefooghe, Arnaud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Verel, Sebastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]

Éditeur(s) ou directeur(s) scientifique(s) :

Heike Trautmann
Günter Rudolph
Kathrin Klamroth
Oliver Schütze
Margaret Wiecek
Yaochu Jin
Christian Grimme

Titre de la manifestation scientifique :

9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017)

Ville :

Münster

Pays :

Allemagne

Date de début de la manifestation scientifique :

2017-03-19

Titre de l’ouvrage :

Evolutionary Multi-Criterion Optimization9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings

Titre de la revue :

Lecture Notes in Computer Science (LNCS)

Éditeur :

Springer

Date de publication :

2017-02

Discipline(s) HAL :

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

Résumé en anglais : [en]

We study the difficulty of solving different bi-objective formulations of the permutation flowshop scheduling problem by adopting a fitness landscape analysis perspective. Our main goal is to shed the light on how different ...
Lire la suite >We study the difficulty of solving different bi-objective formulations of the permutation flowshop scheduling problem by adopting a fitness landscape analysis perspective. Our main goal is to shed the light on how different problem features can impact the performance of Pareto local search algorithms. Specifically, we conduct an empirical analysis addressing the challenging question of quantifying the individual effect and the joint impact of different problem features on the success rate of the considered approaches. Our findings support that multi-objective fitness landscapes enable to devise sound general-purpose features for assessing the expected difficulty in solving permutation flowshop scheduling problems, hence pushing a step towards a better understanding of the challenges that multi-objective randomized search heuristics have to face.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL