Abstract
Graph coloring problem (GCP) is a well-known NP-hard combinatorial optimization problem in graph theory. Solution for GCP often finds its applications to various engineering fields. So it is very important to find a feasible solution quickly. Recent years, Compute Unified Device Architecture (CUDA) show tremendous computational power by allowing parallel high performance computing. In this paper, we present a novel parallel genetic algorithm to solve the GCP based on CUDA. The initialization, crossover, mutation and selection operators are designed parallel in threads. Moreover, the performance of our algorithm is compared with the other graph coloring methods using standard DIMACS benchmarking graphs, and the comparison result shows that our algorithm is more competitive with computation time and graph instances size.
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
Preview
Unable to display preview. Download preview PDF.
References
Hertz, A., Werra, D.: Using Tabu Search Techniques for Graph Coloring. Computing 39, 345–351 (1987)
Lucet, C., Mendes, F., Moukrim, A.: An Exact Method for Graph Coloring. Computers and Operations Research 33(8), 2189–2207 (2006)
Morgenstern, C.: Distributed Coloration Neighborhood Search. In: Cliques, Coloring and Satis-fiability – Second DIMACS Implementation Challenge 1993, vol. 26, pp. 335–358. American Mathematical Society (1996)
Brelaz, D.: New Methods to Color the Vertices of a Graph. Communications of the ACM 22(4), 251–256 (1979)
De, W.D.: An Introduction to Timetabling. European Journal of Operational Research 19, 151–162 (1985)
Luebke, D., Santa, C.: CUDA Scalable Parallel Programming for High-Performance Scientific Computing. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 836–838 (2008)
Hermann, F., Hertz, A.: Finding the Chromatic Number by Means of Critical Graphs. ACM Journal of Experimental Algorithms 7(10), 1–9 (2002)
Shi, X., Lu, W., Wang, Z.: Programmable DNA Tile Self-assembly Using a Hierarchical Sub-tile Strategy. Nanotechnology 25(7), 075602 (2014)
Culberson, J., Luo, J.: Exploring the k-colorable Landscape with Iterated Greedy. Cliques. In: Coloring and Satisfiability - Second DIMACS Implementation Challenge, American Mathematical Society, vol. 26, pp. 245–284 (1996)
Smith, K., Palaniswami, M.: Static and Dynamic Channel Assignment Using Neural Networks. IEEE J. Select. Areas Commun. 15, C238–C249 (1997)
Bianco, L., Caramia, M., Dell’Olmo, M.: Solving a Preemptive Scheduling Problem Using Coloring Technique. In: Project Scheduling Recent Models, Algorithms and Applications. Kluwer Academic Publishers (1998)
Caramia, M., DellOlmo, M.: Iterative Coloring Extension of a Maximum Clique. Naval Research Logistics 48, 518–550 (2001)
Noise Reduction in VLSI Circuits using Modified GA Based Graph Coloring. International Journal of Control and Automation 3(2) (2010)
Kannan, S., Proebsting, T.: Register allocation in structured programs. Journal of Algorithms 29, 223–237 (1998)
Thang, N., Bui, T.H., Nguyen, C.M.P., Kim-Anh, T.P.: An Ant-based Algorithm for Coloring Graphs. Discrete Applied Mathematics 156, 190–200 (2008)
White, T., Pagurek, B., Oppacher, F.: Improving the Ant System by Integration with Genetic Algorithms. In: Proceedings of the 3rd Conference on Genetic Programming, vol. 7, pp. 610–617 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, K., Qiu, M., Li, L., Liu, X. (2014). Accelerating Genetic Algorithm for Solving Graph Coloring Problem Based on CUDA Architecture. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_95
Download citation
DOI: https://doi.org/10.1007/978-3-662-45049-9_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45048-2
Online ISBN: 978-3-662-45049-9
eBook Packages: Computer ScienceComputer Science (R0)