Abstract
A distributed algorithm to find a maximal independent set of an undirected graph is proposed. It is borrowed by a centralized one and it is based on a sequence of Hopfield neural networks. We refer to the synchronous model of distributed computation in which the topology is described by the graph. We give an upper bound on the number of messages sent during the entire process of computation.
To test the algorithm we experimentally compare it with a probabilistic heuristic derived by Ant Colony Optimization technique and with the standard greedy algorithm.
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Grossi, G., Posenato, R. (2002). A Distributed Algorithm for Max Independent Set Problem Based on Hopfield Networks. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_6
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DOI: https://doi.org/10.1007/3-540-45808-5_6
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