Shrinking the Network: The BlåtAnt Algorithm

2011

This paper presents two new methods for network analysis. Ant colony optimization is a nature inspired algorithm succesfull in graph traversal and network path finding whereas network reduction based on stability introduces two new properties of network vertices based on their long-term behavior, their role in the network and the understanding of how memory works. We illustrate the algorithms on applications in social network analysis and information retrieval using the DBLP dataset and a small network of hyperlinked documents.

Journal of the Franklin Institute, 2006

Appropriate routing in data transfer is a challenging problem that can lead to improved performance of networks in terms of lower delay in delivery of packets and higher throughput. Considering the highly distributed nature of networks, several multi-agent based algorithms, and in particular ant colony based algorithms, have been suggested in recent years. However, considering the need for quick optimization and adaptation to network changes, improving the relative slow convergence of these algorithms remains an elusive challenge. Our goal here is to reduce the time needed for convergence and to accelerate the routing algorithm's response to network failures and/or changes by imitating pheromone propagation in natural ant colonies. More specifically, information exchange among neighboring nodes is facilitated by proposing a new type of ant (helping ants) to the AntNet algorithm. The resulting algorithm, the ''modified AntNet,'' is then simulated via NS2 on NSF network topology. The network performance is evaluated under various node-failure and nodeadded conditions. Statistical analysis of results confirms that the new method can significantly reduce the average packet delivery time and rate of convergence to the optimal route when compared with standard AntNet.

Lecture Notes in Computer Science, 2004

Designing an optimal overlay communication network for a set of processes on the Internet is a central problem of peer-to-peer (P2P) computing. Such a network defines membership and allows for members to disseminate information within the group. The network has to be robust and the available bandwidth has to be utilized in an optimal manner to allow for maximally efficient communication. This problem can be formulated as a dynamic optimization problem where classical combinatorial optimization techniques must face the further challenge of time-varying input data. ACO systems appear to be particularly fit for this class of problems, being able to construct an internal model of the instance to face and to exploit it for fast adaptation to modified contexts. This paper proposes to use elements resulting from mathematical techniques, in this case Lagrangean relaxation, in an ACO framework in order to achieve sound hot start states for fast response to varying network structures.

International Journal of Computational Intelligence Systems, 2014

Network reconfiguration of a power distribution system is an operation to alter the topological structure of distribution feeders by changing open/closed status of sectionalizing and tie switches. Network reconfiguration balances feeder loads and helps in managing overload conditions of the network by transferring load from heavily loaded feeders to lightly loaded ones. In this paper we have introduced an ant colony system algorithm for performing network reconfiguration efficiently so as to minimize power losses occurring in a distribution network. The main idea is that of having a set of agents, called ants, which perform search in parallel for good solutions and cooperate through pheromone-mediated indirect and global communication. Informally, each ant constructs a solution path in an iterative way. Validation of the proposed algorithm has been carried using a standard IEEE network. The results found are satisfactory and prove ant colony system algorithm to be an efficient tool for optimal network reconfiguration.

2013

There has been a lot of research on the application of Swarm Intelligence to the problem of adaptive routing in telecommunications networks in the recent past. A large number of algorithms have been proposed for different types of networks, including wired networks and wireless ad hoc networks. In this paper we focus on the application of Swarm Intelligence design to one particular class of optimization problems, namely adaptive routing in telecommunications networks .We discuss both the principles underlying the research and the practical applications that have been proposed. Then we present Ant Colony Routing Algorithm which is used for evaluating minimum cost function for a given network which is adaptive.

2004

We introduce new distributed algorithms that dynamically construct network topologies. These algorithms not only adapt to dynamic topologies where nodes join and leave, but also actively set up and remove links between the nodes, to achieve certain global graph properties. First, we present a novel distributed algorithm for constructing overlay networks that are composed of d Hamilton cycles. The protocol is decentralized as no globally-known server is required. With high probability, the constructed topologies are expanders with O(log dn) diameters and 22d-1+e second largest eigenvalues. Our protocol exploits the properties of random walks on expanders. A new node can join the network in O(log dn) time with O(d log dn) messages. A node can leave in O(1) time with O(d) messages. Second, we investigate a layered construction of the random expander networks that can implement a distributed hash table. Layered expanders can achieve degree-optimal routing at O(log n/log log n) time, whe...

2008

Various forms of swarm intelligence are inspired by social behavior of insects that live collectively. AntNet is a form of such social algorithms, but it has a scalability problem with growing network size. If every node sends only one ant to each destination node and there are N nodes in the network, the total number of ants that are sent is N(N-1). In addition with increasing overhead for large networks, most of the ants are often lost for distant destinations. Furthermore, due to long travel times, ants that do arrive may carry outdated information. In this paper, a novel hierarchical algorithm is proposed to resolve this scalability problem of AntNet. The proposed Super-AntNet divides a large scale network into several small networks that are chosen based their internal traffic patterns. A separate ant colony is then assigned to each of these networks. A Super-Ant Colony is then responsible to coordinate data routing among the colonies. Performance of Super-AntNet is compared with those of standard AntNet as well as two other conventional routing algorithms such as Distance Vector (DV) and Link State (LS) in terms of end-toend delay, throughput, packet loss ratio, increased overhead, as well as jitter. Application to a 16-node network indicates the superiority of the proposed algorithm. I. INTRODUCTION wide variety of routing algorithms exist for communication networks. In traditional routing, routing tables are updated by exchanging routing information among the routers. In Distance-vector (DV) based routing, for instance, routing tables are exchanged, and in Link-State (LS), link state information is flooded over the network. More recently, mobile agents have been used to network routing by inspiration from ant routing algorithms. As Dhillon and Van mieghem [1] gave the general name ANTRAL (ANT Routing Algorithms) to hop-by-hop routing algorithms based on the stigmergic communication found in natural ant colonies. Stigmergy is a form of indirect communication by modifying the environment [2,4]. AntNet is a type of ANTRAL that was first proposed by Di Caro and Dorigo [4] in 1998. AntNet is a promising routing paradigm due to its complete distributed nature of information dissemination and is shown to provide good adaptation during network failures. But AntNet has a growing scalability problem with larger scale networks, because each node has to generate many ants for updating its routing table. In large networks, both over head and ant loss Saeed Saffari Aman is with the Switching Center 1 (SC1) of Mashhad, Korasane Razavi Telecom.

Studies in computational intelligence, 2012

Overlay networks are virtual networks of nodes and logical links built on top of the existing network infrastructure, with the purpose of contributing new functionality. There are many different solutions proposed to tackle a range of specific needs such as content distribution and caching, file sharing, improved routing, multicast and streaming, ordered message delivery, and enhanced security and privacy. In this chapter, the focus lies on the optimization of overlay networks in terms of cost, performance, and reliability. In particular, the main objective is the optimization of data mirroring. Three different optimization approaches are introduced. The first approach is based on a "related work" implementation using Genetic algorithms, the second makes use of artificial immune systems, and the third approach uses the Particle swarm optimization approach. The three algorithms are implemented and experiments are conducted to measure the overall performance, the behavior and feasibility of network and link failures, as well as a scalability analysis is performed.

1998

In this paper we present AntNet, a novel adaptive approach to routing tables learning in packet-switched communications networks. AntNet is inspired by the stigmergy model of communication observed in ant colonies.

In ant societies, and, more in general, in insect societies, the activities of the individuals, as well as of the society as a whole, are not regulated by any explicit form of centralized control. On the other hand, adaptive and robust behaviors transcending the behavioral repertoire of the single individual can be easily observed at society level. These complex global behaviors are the result of self-organizing dynamics driven by local interactions and communications among a number of relatively simple individuals. The simultaneous presence of these and other fascinating and unique characteristics have made ant societies an attractive and inspiring model for building new algorithms and new multi-agent systems. In the last decade, ant societies have been taken as a reference for an ever growing body of scientific work, mostly in the fields of robotics, operations research, and telecommunications.