Abstract
Recent works in combinatorial optimization shows that the cooperation of activities allows obtaining good results. In this work, we are interested in a parallel cooperation between Ant Colony System (ACS) and Marriage in honey Bees Optimisation (MBO) for the resolution of the graph coloring problem (GCP). We first present two ACS new strategies (ACS1 and ASC2) and an MBO approach (BeesCol) for the GCP, then, we offer several collaboration modes and parallelisation for the proposed methods using a parallel machine simulated on a cluster of PCs. An empirical study is undertaken for each method. Moreover, to test our approach, we have also implemented effective algorithms for the GCP. A comparison between the different algorithms shows that ACS1 (construction strategy) gives best results and is quite fast compared to other methods. Moreover, the parallel implementation of ACS reduces significantly the execution time. Finally, we show that the cooperation between ACS and MBO improves the results obtained separately by each algorithm.
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Bessedik, M., Daoudi, A., Benatchba, K. (2014). A Cooperative Approach Using Ants and Bees for the Graph Coloring Problem. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_14
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DOI: https://doi.org/10.1007/978-3-319-01692-4_14
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