Titre :

Distributed Localized Bi-objective Search

Auteur(s) :

Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Humeau, Jérémie [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]

Titre de la revue :

European Journal of Operational Research

Pagination :

731-743

Éditeur :

Elsevier

Date de publication :

2014-12

ISSN :

0377-2217

Mot(s)-clé(s) en anglais :

Multiple objective programming
Combinatorial optimization
Parallel and distributed computing
Evolutionary computation

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

We propose a new distributed heuristic for approximating the Pareto set of bi-objective optimization problems. Our approach is at the crossroads of parallel cooperative computation, objective space decomposition, and ...
Lire la suite >We propose a new distributed heuristic for approximating the Pareto set of bi-objective optimization problems. Our approach is at the crossroads of parallel cooperative computation, objective space decomposition, and adaptive search. Given a number of computing nodes, we self-coordinate them locally, in order to cooperatively search different regions of the Pareto front. This offers a trade-off between a fully independent approach, where each node would operate independently of the others, and a fully centralized approach, where a global knowledge of the entire population is required at every step. More specifically, the population of solutions is structured and mapped into computing nodes. As local information, every node uses only the positions of its neighbors in the objective space and evolves its local solution based on what we term a 'localized fitness function'. This has the effect of making the distributed search evolve, over all nodes, to a high quality approximation set, with minimum communications. We deploy our distributed algorithm using a computer cluster of hundreds of cores and study its properties and performance on rhoMNK-landscapes. Through extensive large-scale experiments, our approach is shown to be very effective in terms of approximation quality, computational time and scalability.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