When the Decomposition Meets the Constraint Satisfaction Problem
Peer reviewed, Journal article
MetadataShow full item record
Original versionDjenouri, Y., Djenouri, D., Habbas, Z., Lin, J. C.-W., Michalak, T. P., & Cano, A. (2020). When the decomposition meets the constraint satisfaction problem. IEEE Access, 8, 207034-207043. 10.1109/ACCESS.2020.3038228
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.