Abstract
Solving a NP-Complete problem precisely is spiny: the combinative explosion is the ransom of this accurateness. It is the reason for which we have often resort to approached methods assuring the obtaining of a good solution in a reasonable time. In this paper we aim to introduce a new intelligent approach or meta-heuristic named “Bees Swarm Optimization”, BSO for short, which is inspired from the behaviour of real bees. An adaptation to the features of the MAX-W-SAT problem is done to contribute to its resolution. We provide an overview of the results of empirical tests performed on the hard Johnson benchmark. A comparative study with well known procedures for MAX-W-SAT is done and shows that BSO outperforms the other evolutionary algorithms especially AC-SAT, an ant colony algorithm for SAT.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)
Bonabeau, E., Theraulaz, G.: Intelligence collective. Editions Hérmes (1994)
Drias, H., Khabzaoui, M.: Scatter Search with Random Walk Strategy for Sat and Max-Sat Problems. Springer, Heidelberg (2001)
Drias, H., Taibi, A., Zekour, S.: Cooperative Ant Colonies for Solving the Maximum Weighted Satisfiability Problem. Springer, Heidelberg (2003)
Johnson, D.S.: Approximate algorithms for combinatorial problems, JCSS, 256-278 (1974)
Ptisouli, L., Paradalos, P.M., Resende, M.G.: Approximate solution of weighted MAX-SAT problems Using GRASP AT & Research, Florham Park, NJ 07932 USA (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Drias, H., Sadeg, S., Yahi, S. (2005). Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_39
Download citation
DOI: https://doi.org/10.1007/11494669_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)