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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Alahmadi, A.A. Alamri, T.M. Hosny, M.I. |
| Copyright Year | 2014 |
| Description | Author affiliation: Comput. Sci. Dept., King Saud Univ., Riyadh, Saudi Arabia (Alahmadi, A.A.; Alamri, T.M.; Hosny, M.I.) |
| Abstract | The Graph Coloring Problem (GCP) is an important practical problem which belongs to the NP-hard class. It is a constraint satisfaction problem that paints a graph using a minimal number of colors, where any two adjacent vertices should have different colors. Most state-of-the-art metaheuristics methods for the GCP start by one or more infeasible solutions and then attempt to obtain a feasible one. In contrast, this paper proposes a performance competitive Demon Algorithm (DA) that starts with a feasible solution, obtained by a greedy algorithm, and tries to maintain feasibility, while the number of colors used to color the graph is reduced one at a time. The enhancement of performance is related to the way that the DA stops searching once a feasible solution is obtained after each color reduction. The paper spotlights the differences in the performance when applying the same algorithm using Simulated Annealing (SA) as well as Threshold Acceptance (TA) algorithms. Experiments carried out on instances of DIMACS benchmark showed that the proposed DA succeeds to achieve the best known results with very efficient time performance. |
| Starting Page | 254 |
| Ending Page | 259 |
| File Size | 918582 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780989319331 |
| e-ISBN | 9780989319317 |
| DOI | 10.1109/SAI.2014.6918198 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-27 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | The Science and Information (SAI) Organization |
| Subject Keyword | Algorithm design and analysis Graph coloring Heuristic algorithms Sociology Metaheuristics Demon algorithm Color Simulated annealing Search problems Combinatorial optimization Statistics |
| Content Type | Text |
| Resource Type | Article |
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