Probabilistic Tabu search with multiple neighborhoods for the Disjunctively Constrained Knapsack Problem - Université Polytechnique des Hauts-de-France Accéder directement au contenu
Article Dans Une Revue RAIRO - Operations Research Année : 2017

Probabilistic Tabu search with multiple neighborhoods for the Disjunctively Constrained Knapsack Problem

Résumé

Given a set of items, each with a profit and a weight and a conflict graph describing incompatibilities between items, the Disjunctively Constrained Knapsack Problem is to select the maximum profit set of compatible items while satisfying the knapsack capacity constraint. We develop a probabilistic tabu search heuristic with multiple neighborhood structures. The proposed algorithm is evaluated on a total of 50 benchmark instances from the literature up to 1000 items. Computational results disclose that the proposed tabu search method outperforms recent state-of-the-art approaches. In particular, our approach is able to reach 46 best known solutions and discover 8 new best known solutions out of 50 benchmark instances.

Dates et versions

hal-03401130 , version 1 (25-10-2021)

Identifiants

Citer

Mariem Ben Salem, Said Hanafi, Raouia Taktak, Hanene Ben-Abdallah. Probabilistic Tabu search with multiple neighborhoods for the Disjunctively Constrained Knapsack Problem. RAIRO - Operations Research, 2017, 51 (3), pp.627-637. ⟨10.1051/ro/2016049⟩. ⟨hal-03401130⟩
11 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More