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Article Dans Une Revue IEEE Access Année : 2020

When the Decomposition Meets the Constraint Satisfaction Problem

Résumé

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is that the set of constraints are first clustered using a decomposition algorithm in which highly correlated constraints are grouped together. Next, parallel search of variables is performed on the produced clusters in a way that is friendly for parallel computing. In particular, for the first step, we propose the adaptation of two well-known clustering algorithms (k-means and DBSCAN). For the second step, we develop a GPU-based approach to efficiently explore the clusters. The results from the extensive experimental evaluation show that the PD-CSP provides competitive results in terms of accuracy and runtime.

Dates et versions

hal-03251425 , version 1 (07-06-2021)

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Citer

Youcef Djenouri, Djamel Djenouri, Zineb Habbas, Jerry Chun-Wei Lin, Tomasz Michalak, et al.. When the Decomposition Meets the Constraint Satisfaction Problem. IEEE Access, 2020, 8, pp.207034-207043. ⟨10.1109/ACCESS.2020.3038228⟩. ⟨hal-03251425⟩
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