Parallel Variable Neighborhood Search

Abstract

Les textes publiés dans la série des rapports de recherche HEC n'engagent que la responsabilité de leurs auteurs. La publication de ces rapports de recherche bénéficie d'une subvention du Fonds québécois de la recherche sur la nature et les technologies. AbstractVariable Neighborhood Search (VNS) is a recent and effective metaheuristic for solving combinatorial and global optimization problems. It is capable of escaping from the local optima by systematic changes of the neighborhood structures within the search. In this paper several parallelization strategies for VNS have been proposed and compared on the large instances of the p-median problem. RésuméLa Rechercheà Voisinage Variable (RVV) est une métaheuristique récente et efficace pour la résolution de problèmes d'optimisation combinatoire ou globale. Elle permet de quitter un optimum local par des changements systématiques de structures de voisinageà l'intérieur de la recherche. Pour cet article plusieurs stratégies de parallélisation de RVV sont proposées et composées sur de grandes instances de problème de la p-médiane.

Parallel Local Search to schedule communicating tasks on identical processors

“…The final step is used to verify if the stopping criterion is met. Recent developments and applications of VNS may be found in [17,27].…”

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

“…Most of the reviews that we found focus on parallel optimization regarding particular methodologies. While branch-and-bound algorithms have been reviewed by Gendron and Crainic [1994], the majority of methodological literature reviews have focused on metaheuristics: reviews have addressed tabu search (TS) [Crainic et al, 2005], simulated annealing (SA) [Aydin and Yigit, 2005], variable neighborhood search (VNS) [Pérez et al, 2005], Greedy Randomized Adaptive Search Procedures (GRASPs) [Resende and Ribeiro, 2005], swarm intelligence algorithms [Tan and Ding, 2016], particle swarm optimization algorithms [Zhang et al, 2015], and different types of evolutionary algorithms, including genetic algorithms (GAs) [Adamidis, ]. Application-and discipline-oriented reviews of parallel optimization have been provided for routing problems in logistics and for parallel metaheuristics in the fields of telecommunications and bioinformatics [Nesmachnow et al, 2005, Trelles and Rodriguez, 2005, Martins and Ribeiro, 2006].…”