28,330 research outputs found
A Neural Network for Shortest Path Computation
This paper presents a new neural network to solve the shortest path problem for internetwork routing. The proposed solution extends the traditional single-layer recurrent Hopfield architecture introducing a two-layer architecture that automatically guarantees an entire set of constraints held by any valid solution to the shortest path problem. This new method addresses some of the limitations of previous solutions, in particular the lack of reliability in what concerns succeeded and valid convergence. Experimental results show that a clear improvement in well-succeeded convergence can be achieved. Additionally, computation performance is also improved at the expense of slightly worse result
Delay-aware Backpressure Routing Using Graph Neural Networks
We propose a throughput-optimal biased backpressure (BP) algorithm for
routing, where the bias is learned through a graph neural network that seeks to
minimize end-to-end delay. Classical BP routing provides a simple yet powerful
distributed solution for resource allocation in wireless multi-hop networks but
has poor delay performance. A low-cost approach to improve this delay
performance is to favor shorter paths by incorporating pre-defined biases in
the BP computation, such as a bias based on the shortest path (hop) distance to
the destination. In this work, we improve upon the widely-used metric of hop
distance (and its variants) for the shortest path bias by introducing a bias
based on the link duty cycle, which we predict using a graph convolutional
neural network. Numerical results show that our approach can improve the delay
performance compared to classical BP and existing BP alternatives based on
pre-defined bias while being adaptive to interference density. In terms of
complexity, our distributed implementation only introduces a one-time overhead
(linear in the number of devices in the network) compared to classical BP, and
a constant overhead compared to the lowest-complexity existing bias-based BP
algorithms.Comment: 5 pages, 5 figures, submitted to IEEE ICASSP 202
A Potts Neuron Approach to Communication Routing
A feedback neural network approach to communication routing problems is
developed with emphasis on Multiple Shortest Path problems, with several
requests for transmissions between distinct start- and endnodes. The basic
ingredients are a set of Potts neurons for each request, with interactions
designed to minimize path lengths and to prevent overloading of network arcs.
The topological nature of the problem is conveniently handled using a
propagator matrix approach. Although the constraints are global, the
algorithmic steps are based entirely on local information, facilitating
distributed implementations. In the polynomially solvable single-request case
the approach reduces to a fuzzy version of the Bellman-Ford algorithm. The
approach is evaluated for synthetic problems of varying sizes and load levels,
by comparing with exact solutions from a branch-and-bound method. With very few
exceptions, the Potts approach gives legal solutions of very high quality. The
computational demand scales merely as the product of the numbers of requests,
nodes, and arcs.Comment: 10 pages LaTe
Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks
This article is posted here with permission from the IEEE - Copyright @ 2009 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless sensor network (WSN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem (DOP) in MANETs. In this paper, we propose to use elitism-based immigrants GA (EIGA) to solve the dynamic SP problem in MANETs. We consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
Brain architecture: A design for natural computation
Fifty years ago, John von Neumann compared the architecture of the brain with
that of computers that he invented and which is still in use today. In those
days, the organisation of computers was based on concepts of brain
organisation. Here, we give an update on current results on the global
organisation of neural systems. For neural systems, we outline how the spatial
and topological architecture of neuronal and cortical networks facilitates
robustness against failures, fast processing, and balanced network activation.
Finally, we discuss mechanisms of self-organization for such architectures.
After all, the organization of the brain might again inspire computer
architecture
Two betweenness centrality measures based on Randomized Shortest Paths
This paper introduces two new closely related betweenness centrality measures
based on the Randomized Shortest Paths (RSP) framework, which fill a gap
between traditional network centrality measures based on shortest paths and
more recent methods considering random walks or current flows. The framework
defines Boltzmann probability distributions over paths of the network which
focus on the shortest paths, but also take into account longer paths depending
on an inverse temperature parameter. RSP's have previously proven to be useful
in defining distance measures on networks. In this work we study their utility
in quantifying the importance of the nodes of a network. The proposed RSP
betweenness centralities combine, in an optimal way, the ideas of using the
shortest and purely random paths for analysing the roles of network nodes,
avoiding issues involving these two paradigms. We present the derivations of
these measures and how they can be computed in an efficient way. In addition,
we show with real world examples the potential of the RSP betweenness
centralities in identifying interesting nodes of a network that more
traditional methods might fail to notice.Comment: Minor updates; published in Scientific Report
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