Authors:
Youcef Djenouri
1
;
Zineb Habbas
2
and
Wassila Aggoune-Mtalaa
3
Affiliations:
1
Saad Dahleb University, Algeria
;
2
University of Lorraine, France
;
3
LIST and Luxembourg Institute of Science and Technology G.D., Luxembourg
Keyword(s):
BSO, MAX-SAT, Decomposition Methods, Kmeans, BSOGD
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Constraint Satisfaction
;
Symbolic Systems
Abstract:
Decomposition methods aim to split a problem into a collection a collection of smaller interconnected sub-problems.
Several research works have explored decomposition methods for solving large optimization problems.
Due to its theroretical properties, Tree decomposition has been especially the subject of numerous
successfull studies in the context of exact optimization solvers. More recently, Tree decomposition has been
successfully used to guide the Variable Neighbor Search (VNS) local search method. Our present contribution
follows this last direction and proposes two approaches called BSOGD1 and BSOGD2 for guiding the Bees
Swarm Optimization (BSO) metaheuristic by using a decomposition method. More pragmatically, this paper
deals with the MAX-SAT problem and uses the Kmeans algorithm as a decomposition method. Several experimental
results conducted on DIMACS benchmarks and some other hard SAT instances lead to promising
results in terms of the quality of the solutions. Moreover, t
hese experiments highlight a good stability of the
two approaches, more especially, when dealing with hard instances like the Parity8 family from DIMACS.
Beyond these first promising results, note that this approach can be easily applied to many other optimization
problems such as the Weighted MAX-SAT, the MAX-CSP or the coloring problem and can be used with other
decomposition methods as well as other metaheuristics.
(More)