Abstract
Metaheuristics and especially Swarm Intelligence represents one of the mostly used aspect of Artificial Intelligence. In fact, these algorithms are exploited in several domains from theoretical problem solving to air traffic management. The evaluation of such methods is defined by the quality of solution they provide or effectiveness and the spent time to reach this solution or efficiency.
We explore, in this paper, the technology offered by the Graphic Processing Unit -GPU- to improve the efficiency of the Bees Swarm Optimization algorithm -BSO- by proposing a novel and parallel CPU/GPU version of the later algorithm. The algorithm being greedy when the problem size is important, which is almost always the case.
The proposed parallel algorithm is integrated in the proposed method of clustering-solving hard problems presented in [1], adding the exploitation of GPU performance to that of data mining to improve the resolution of hard and complex problems such as Satisfiability problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hireche, C., Drias, H.: Density based clustering for satisfiability solving. In: World Conference on Information Systems and Technologies, pp. 899-908. Springer, Heidelberg (2018)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide of the Theory of NP-Completeness. A Series of Books in the Mathematical Sciences, p. x+338. W.H. Freeman and Co., New York (1979). ISBN 0-7167-1045-5. MR 519066
Cook, S.: The complexity of theorem-proving procedures. In: Proceeding 3rd Annual ACM Symposium on the Theory of Computing, New York, pp. 151-198 (1971)
Glover, F., Kochenberger, G.A.: Handbook of Metaheuristics. Springer, US (2005). ISBN: 978-1-4020-7263-5. https://doi.org/10.1007/b101874.
Drias, H., Sadeg, S., Yahi, S.: Cooperatives bees swarm for solving the maximum weighted satisfiability problem. In: Proceeding of IWANN 2005, LNCS, vol. 3512, pp. 318-325. Springer, Barcelona (2005)
Seeley, T.D., Camazine, S., Sneyd, J.: Collective decision-making in honey bees: how colonies choose among nectar sources. Behav. Ecol. Sociobiol. 28, 277–290 (1991)
NVIDIA CUDA C programming guide version 4.0. Nvidia Corporation (2012)
Kirk, D.B., Wen-Mei, W.H.: Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann, Burlington (2016)
Han, J., et al.: Data mining, concepts and techniques. Third Edition (The Morgan Kaufmann Series in Data Management Systems) (2011)
Davis, M., Logemann, G., Loveland, D.: A machine program for theorem proving. Commun. ACM 5(7), 394–397 (1962)
Biere, A., Cimatti, A., Clarke, E., Zhu, Y.: Symbolic model checking without BDDs. In: The Proceedings of the Workshop on Tools and Algorithms for the Construction and Analysis of Systems (TACAS99), LNCS. Springer, Heidelberg (1999)
Artificially Generated Random. https://baldur.iti.kit.edu/sat-competition-2016/index.php?cat=benchmarks
Random SAT. https://baldur.iti.kit.edu/sat-competition-2016/index.php?cat=benchmarks
Hireche, C., Drias, H.: Multidimensional appropriate clustering and DBSCAN for SAT solving . Data Technol. Appl. J. (2019). Emerald Publishing Limited
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hireche, C., Drias, H. (2020). A Parallel CPU/GPU Bees Swarm Optimization Algorithm for the Satisfiability Problem. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1160. Springer, Cham. https://doi.org/10.1007/978-3-030-45691-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-45691-7_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45690-0
Online ISBN: 978-3-030-45691-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)