On a parallel genetic–tabu search based algorithm for solving the graph colouring problem

In this paper, we are interested in a particular combinatorial optimisation problem (COP), namely the graph colouring problem (GCP). To solve the GCP, we present a parallel approach adopting an efficient strategy. A brief survey on known methods for solving the GCP enables us to justify our approach...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 197; no. 3; pp. 1192 - 1201
Main Authors Mabrouk, Bchira Ben, Hasni, Hamadi, Mahjoub, Zaher
Format Journal Article Conference Proceeding
LanguageEnglish
Published Amsterdam Elsevier B.V 16.09.2009
Elsevier
Elsevier Sequoia S.A
SeriesEuropean Journal of Operational Research
Subjects
Applied sciences
Benchmarks
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Genetic algorithms Graph colouring Hybridation Parallelisation Tabu search
Graph coloring
Graph colouring
Graphs
Hybridation
Operational research and scientific management
Operational research. Management science
Parallelisation
Problem solving
Studies
Tabu search
Hybridation
Graph colouring
Tabu search
Parallelisation
Genetic algorithms
Combinatorial problem
Combinatorial optimization
Chromatic number
Search algorithm
Random graph
Multiprocessor
Genetic algorithm
Chromatic graph
Internet
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Summary:In this paper, we are interested in a particular combinatorial optimisation problem (COP), namely the graph colouring problem (GCP). To solve the GCP, we present a parallel approach adopting an efficient strategy. A brief survey on known methods for solving the GCP enables us to justify our approach which is based on a hybrid method, starting from a set of solutions initialized by the so-called RLF colouring method and combining both a genetic algorithm and the tabu search. A parallelising strategy is then applied. The performances of our method were evaluated through a series of experimentations achieved on an IBM SP2 multiprocessor. The processed graphs were chosen from two benchmark sets. The first, taken from the Internet, involves graphs whose chromatic numbers are known and the second involves random generated graphs. The analysis of the results proves the interest of our approach.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2008.03.050