AEDB Protocol Tuning with a Fast Efficient Parallel Multi-Objective Local Search

Iturriaga, Santiago; Nesmachnow, Sergio; Ruiz, Patricia; Bouvry, Pascal; Dorronsoro, Bernabé

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1504/IJAHUC.2014.065775

Titre :

AEDB Protocol Tuning with a Fast Efficient Parallel Multi-Objective Local Search

Auteur(s) :

Iturriaga, Santiago [Auteur]
UDELAR, Facultad de Ingenieria [Montevideo] [UDELAR]
Nesmachnow, Sergio [Auteur]
UDELAR, Facultad de Ingenieria [Montevideo] [UDELAR]
Ruiz, Patricia [Auteur]
Computer Science and Communications Research Unit [Luxembourg] [CSC]
Bouvry, Pascal [Auteur]
Université du Luxembourg = University of Luxembourg = Universität Luxemburg [uni.lu]
Dorronsoro, Bernabé [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]

Titre de la revue :

IJAHUC - International Journal of Ad Hoc and Ubiquitous Computing

Pagination :

144-161

Éditeur :

Inderscience

Date de publication :

2014-11

ISSN :

1743-8225

Discipline(s) HAL :

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

Résumé en anglais : [en]

Mobile ad hoc networks (MANETs) are infrastructure-less communication networks spontaneously created by a number of mobile devices. Due to its highly fluctuating topology, finding the optimal configuration of communication ...
Lire la suite >Mobile ad hoc networks (MANETs) are infrastructure-less communication networks spontaneously created by a number of mobile devices. Due to its highly fluctuating topology, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. In our previous work, we introduced a novel parallel multi-objective local search to optimise the AEDB broadcasting protocol in terms of coverage, energy used, broadcasting time, and network resources. In this paper, we extend our previous work by studying three different versions of the problem, that are restricting the minimum coverage value for valid solutions, in order to accelerate the search and make it focus on the desired area of the search space. Additionally, the convergence speed of the algorithm was analysed, and different population initialisation techniques were compared. The new algorithm is validated vs. efficient state-of-the art multi-objective evolutionary techniques, offering high quality results in much shorter times, especially for high coverage solutions.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

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