Ant-Based Load Balancing in Telecommunications Networks

Schoonderwoerd, Ruud; Bruten, Janet; Holland, Owen; Rothkrantz, Léon J. M.

doi:10.1177/105971239700500203

Cited by 601 publications

(349 citation statements)

References 9 publications

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“…According to the characteristics of the processing and transmission components, the traffic flow pattern, and the performance expected to be delivered of the network, different types of routing problems can be defined. Mainly, there are two categories of network routing problems in which ACO has been applied: wired, e.g., wired best effort networks [211,212,213,214,215] and wired quality of service (QoS) networks [216,217,218,214,219,220], and wireless, e.g., mobile ad hoc networks [221,222,223,224,225]. The telecommunication algorithms simply rely on the adaptation capabilities of ACO.…”

Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Discrete Applicationsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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“…The first ant-based routing algorithms were ABC [14] and AntNet [3]. Both algorithms follow a similar general strategy.…”

Section: Related Work: Manet Routing and Ant-based Routingmentioning

confidence: 99%

“…For wired networks, a number of successful ant-based routing algorithms exist (eg. ABC [14] and AntNet [3]). They are based on the pheromone trail laying-following behavior of real ants and the related framework of ant colony optimization (ACO) [4].…”

Section: Introductionmentioning

confidence: 99%

AntHocNet: An Ant-Based Hybrid Routing Algorithm for Mobile Ad Hoc Networks

Ducatelle

Gambardella

2004

Lecture Notes in Computer Science

170

102

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Abstract. In this paper we present AntHocNet, a new algorithm for routing in mobile ad hoc networks. Due to the ever changing topology and limited bandwidth it is very hard to establish and maintain good routes in such networks. Especially reliability and efficiency are important concerns. AntHocNet is based on ideas from Ant Colony Optimization. It consists of both reactive and proactive components. In a reactive path setup phase, multiple paths are set up between the source and destination of a data session, and during the course of the communication session, ants proactively test existing paths and explore new ones. In simulation tests we show that AntHocNet can outperform AODV, one of the most important current state-of-the-art algorithms, both in terms of end-to-end delay and packet delivery ratio.

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“…A wide range of ACO routing algorithms are using for wired networks such as, AntNet [7], and Ant-Based Control (ABC) [8].…”

Section: Aco Routing In Manetsmentioning

confidence: 99%

An Adaptive Multipath Ant Routing algorithm for Mobile Ad Hoc Networks

Samadi¹,

Beigy²

2012

IJCEE

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Abstract-Mobile Ad hoc networks (MANET) are communication networks that consist of wireless nodes, which placed together in an ad hoc manner. Nodes can enter or leave the network at any time, so that the network topology changes frequently. In this paper we are going to propose an algorithm for routing in mobile ad hoc networks. Routing task has some challenging problem in MANETs, based on having dynamic and unplanned nature. For facing with these challenges we propose an Adaptive Multipath Ant Routing algorithm (AMAR) by combining ideas from artificial intelligence (AI) and multipath routing in this algorithm for improving the network performance. In an extensive set of simulation experiments, we compare this algorithm with AntHocNet and AODV and OLSR which are three reference algorithms in this research area.

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Ant-Based Load Balancing in Telecommunications Networks

Cited by 601 publications

References 9 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

AntHocNet: An Ant-Based Hybrid Routing Algorithm for Mobile Ad Hoc Networks

An Adaptive Multipath Ant Routing algorithm for Mobile Ad Hoc Networks

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