MOEAQ: A QoS-Aware Multicast Routing algorithm for MANET

Huang, Jun; Liu, Yanbing

doi:10.1016/j.eswa.2009.06.086

Cited by 47 publications

(22 citation statements)

References 16 publications

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“…The main aim is to quickly identify the efficient routes and the response to the dynamics of the network very fast [26]. Some recent research has been done with the genetic or hybrid approach in MANET routing such as [27][28][29][30][31][32]. Specifically, the authors in [27] propose a hybrid routing intelligent algorithm with an Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO) to refine the various metrics in MANET routing [27].…”

Section: Routing Algorithms Based On Gamentioning

confidence: 99%

A Hybrid Algorithm for Preserving Energy and Delay Routing in Mobile Ad-Hoc Networks

Ahmadi

Shojafar

Khademzadeh

et al. 2015

Wireless Pers Commun

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The Quality of Service (QoS) routing protocol plays a vital role in enabling a mobile network to interconnect wired networks with the QoS support. It has become quite a challenge in mobile networks, like mobile ad-hoc networks, to identify a path that fulfils the QoS requirements, regarding their topology and applications. The QoS routing feature can also function in a stand-alone multi hop mobile network for real-time applications. The chief aim of the QoS aware protocol is to find a route from the source to the destination that fulfils the QoS requirements. In this paper we present a new energy and delay aware routing method which combines Cellular automata (CA) with the Genetic algorithm (GA). Here, two QoS parameters are used for routing; energy and delay. The routing algorithm based on CA is used to identify a set of routes that can fulfill the delay constraints and then select a reasonably good one using GAs. The results of Simulation show that the method proposed produces a higher degree of performance than the AODV and another QoS method in terms of network lifetime and end-to-end delay.

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Section: Routing Algorithms Based On Gamentioning

confidence: 99%

A Hybrid Algorithm for Preserving Energy and Delay Routing in Mobile Ad-Hoc Networks

Ahmadi

Shojafar

Khademzadeh

et al. 2015

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

“…Recent years have witnessed significant progress in the development of evolutionary algorithms (EAs) for multi-objective optimization problems (Huang & Liu, 2010;Knowles & Corne, 2000;Lara, Sanchez, Coello Coello, & Schütze, 2010;Li & Zhang, 2009;Siegfried, Bleuler, Laumanns, Zitzler, & Kinzelbach, 2009;Soylu & Köksalan, 2010;Stoico, Renzi, & Vericat, 2008;Wanner, Guimaraes, Takahashi, Lowther, & Ramirez, 2008;Xue, Sanderson, & Graves, 2009;Yang, Kwan, & Chang, 2008). Multi-objective evolutionary algorithms (MOEAs) aim at finding a set of representative Pareto optimal solutions in a single run.…”

Section: Introductionmentioning

confidence: 99%

High-dimensional objective optimizer: An evolutionary algorithm and its nonlinear analysis

Huang

et al. 2011

Expert Systems with Applications

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“…Recent years have witnessed significant progress in the development of evolutionary algorithms (EAs) for multi-objective optimization problems [2][3][4][5][6][7][8]. Multi-objective evolutionary algorithms (MOEAs) aim at finding a set of representative Pareto optimal solutions in a single run.…”

Section: Introductionmentioning

confidence: 99%

MOEAQ: A QoS-Aware Multicast Routing algorithm for MANET

Cited by 47 publications

References 16 publications

A Hybrid Algorithm for Preserving Energy and Delay Routing in Mobile Ad-Hoc Networks

A Hybrid Algorithm for Preserving Energy and Delay Routing in Mobile Ad-Hoc Networks

High-dimensional objective optimizer: An evolutionary algorithm and its nonlinear analysis

On a high-dimensional objective genetic algorithm and its nonlinear dynamic properties

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