On Integrating Population-Based Metaheuristics with Cooperative Parallelism - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

On Integrating Population-Based Metaheuristics with Cooperative Parallelism

Résumé

Many real-life applications can be formulated as Combinatorial Optimization Problems, the solution of which is often challenging due to their intrinsic difficulty. At present, the most effective methods to address the hardest problems entail the hybridization of metaheuristics and cooperative parallelism. Recently, a framework called CPLS has been proposed, which eases the cooperative parallelization of local search solvers. Being able to run different heuristics in parallel, CPLS has opened a new way to hybridize metaheuristics, thanks to its cooperative parallelism mechanism. However, CPLS is mainly designed for local search methods. In this paper we seek to overcome the current CPLS limitation, extending it to enable population-based metaheuristics in the hybridization process. We discuss an initial prototype implementation for Quadratic Assignment Problem combining a Genetic Algorithm with two local search procedures. Our experiments on hard instances of QAP show that this hybrid solver performs competitively w.r.t. dedicated QAP parallel solvers.
Fichier principal
Vignette du fichier
PDCO-2018.pdf (276.83 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01802097 , version 1 (26-06-2018)

Identifiants

Citer

Jheisson Lopez, Danny Munera, Daniel Diaz, Salvador Abreu. On Integrating Population-Based Metaheuristics with Cooperative Parallelism. 8th IEEE Workshop on Parallel / Distributed Computing and Optimization (PDCO 2018), May 2018, Vancouver, Canada. ⟨10.1109/IPDPSW.2018.00100⟩. ⟨hal-01802097⟩
62 Consultations
261 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More