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.
A distributed algorithm for max independent set problem based on Hopfield networks / G. Grossi, R. Posenato (LECTURE NOTES IN ARTIFICIAL INTELLIGENCE). - In: Neural Nets / [a cura di] M. Marinaro, R. Tagliaferri. - [s.l] : Springer Verlag, 2002. - ISBN 978-3-540-44265-3. - pp. 64-74 (( Intervento presentato al 13. convegno WIRN tenutosi a Vietri sul Mare nel 2002 [10.1007/3-540-45808-5_6].
A distributed algorithm for max independent set problem based on Hopfield networks
G. Grossi;
2002
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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