Paper information
Title
A Diffusion-Based ACO Resource Discovery Framework for Dynamic p2p Networks
A Diffusion-Based ACO Resource Discovery Framework for Dynamic p2p Networks
Kamil Krynicki
Published in
IEEE Congress on Evolutionary Computation - 2013
IEEE Congress on Evolutionary Computation - 2013
Abstract
The Ant Colony Optimization (ACO) has been a very resourceful metaheuristic over the past decade and it has been successfully used to approximately solve many static NP-Hard problems. There is a limit, however, of its applicability in the field of p2p networks; derived from the fact that such networks have the potential to evolve constantly and at a high pace, rendering the already-established results useless. In this paper we approach the problem by proposing a generic knowledge diffusion mechanism that extends the classical ACO paradigm to better deal with the p2p's dynamic nature. Focusing initially on the appearance of new resources in the network we have shown that it is possible to increase the efficiency of ant routing by a significant margin.
The Ant Colony Optimization (ACO) has been a very resourceful metaheuristic over the past decade and it has been successfully used to approximately solve many static NP-Hard problems. There is a limit, however, of its applicability in the field of p2p networks; derived from the fact that such networks have the potential to evolve constantly and at a high pace, rendering the already-established results useless. In this paper we approach the problem by proposing a generic knowledge diffusion mechanism that extends the classical ACO paradigm to better deal with the p2p's dynamic nature. Focusing initially on the appearance of new resources in the network we have shown that it is possible to increase the efficiency of ant routing by a significant margin.
BibTeX
@misc{issi_web:id:428,
title = "A Diffusion-Based ACO Resource Discovery Framework for Dynamic p2p Networks",
author = "Kamil Krynicki and Javier Jaén Martínez and Alejandro Catalá Bolós",
booktitle = "IEEE Congress on Evolutionary Computation",
year = "2013",
eprint = "http://issi.dsic.upv.es/publications/archives/",
url = "",
abstract = "The Ant Colony Optimization (ACO) has been a very resourceful metaheuristic over
the past decade and it has been successfully used to approximately solve many
static NP-Hard problems. There is a limit, however, of its applicability in the
field of p2p networks; derived from the fact that such networks have the
potential to evolve constantly and at a high pace, rendering the
already-established results useless. In this paper we approach the problem by
proposing a generic knowledge diffusion mechanism that extends the classical ACO
paradigm to better deal with the p2p's dynamic nature. Focusing initially on the
appearance of new resources in the network we have shown that it is possible to
increase the efficiency of ant routing by a significant margin."
}