Application of genetic algorithm on quality graded networks for intelligent routing

Nair, T. R. Gopalakrishnan; Sooda, Kavitha

doi:10.1109/wict.2011.6141306

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…This model is based on the concept proposed by Mitola, which is given by Thomas (2005), where the input to the cognitive controller is the data or information collected about the nodes from the network by intelligent agents. These agents can be intelligent routers, sensors or any form of cognitive packet makers where the status of the nodes is collected Nair (2011). The controller then forwards the information to the map developed module, where the graphical representations about the complete nodes participating are formed.…”

Section: Architecture Modelmentioning

confidence: 99%

A QoS-based routing approach using genetic algorithms for bandwidth maximisation in networks

Nair¹,

Sooda²,

Selvarani³

2014

IJAISC

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Abs tract: This paper addresses the path selection problem from a known source to the destination in dense networks. The proposed solution for route discovery uses the genetic algorithm approach for a QoS based network. The multi point crossover and mutation helps in determining the optimal path and alternate path when required. The input to the genetic algorithm is a learnt module which is a part of the cognitive router that takes care of four QoS parameters. Here the set of nodes selected for routing is determined by delay, jitter and loss. On this graded surface of nodes selected, the bandwidth parameter is considered for path selection. The aim of the approach is to occupy the maximized bandwidth along the forward channels and minimize the route length. The population size is considered as fixed nodes participating in the network scenario, which will be limited to a known size of topology. The simulated results show that by using genetic algorithm (GA) approach the probability of convergence to shortest path is higher.

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Section: Architecture Modelmentioning

confidence: 99%

A QoS-based routing approach using genetic algorithms for bandwidth maximisation in networks

Nair¹,

Sooda²,

Selvarani³

2014

IJAISC

Self Cite

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“…This value is subtracted from link capacity to obtain the remaining fraction of the bandwidth available. B a = L c -T l (0); (7) This output is considered by the level-2 operation for calculating the available bandwidth. The traffic intensity is thus determined from Eq.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The concept of graded network has bee authors in [6][7][8]. Grade value estimation m surface creation for implementing intellig autonomic network is a core focus of resea a coagulated index reflection for intelligen in network; it is made available everywher much depend on it.…”

Section: B Graded Networkmentioning

confidence: 99%

“…Here we assume that the traffic arrivals during [0, t] are routed based on the link states T l (0) available at time 0. The link state T l (t) tracks to the following differential equations which derives the total traffic arrival rate γ l to link l. B a = L c -T l (0); (7) This output is considered by the level-2 operation for calculating the available bandwidth. The traffic intensity is thus determined from Eq.…”

Section: Mathematical Modelmentioning

confidence: 99%

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Competitive performance analysis of two evolutionary algorithms for routing optimization in graded network

Sooda¹,

Nair

2013

2013 3rd IEEE International Advance Computing Conference (IACC)

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Abstract-In this paper we compare the two intelligent route generation system and its performance capability in graded networks using Artificial Bee Colony (ABC) algorithm and Genetic Algorithm (GA). Both ABC and GA have found its importance in optimization technique for determining optimal path while routing operations in the network. The paper shows how ABC approach has been utilized for determining the optimal path based on bandwidth availability of the links and determines better quality paths over GA. Here the nodes participating in the routing are evaluated for their QoS metric. The nodes which satisfy the minimum threshold value of the metric are chosen and enabled to participate in routing. A quadrant is synthesized on the source as the centre and depending on which quadrant the destination node belongs to, a search for optimal path is performed. The simulation results show that ABC speeds up local minimum search convergence by around 60% as compared to GA with respect to traffic intensity, and opens the possibility for cognitive routing in future intelligent networks.

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“…This paper is based on the following list of parameters which helps in grading according to priority derived from (Nair and Sooda, 2011) • network lifetime (NL)…”

Section: Implementation Of Gradementioning

confidence: 99%

An intelligent routing approach using genetic algorithms for quality graded network

Nair

Sooda²

2012

IJISTA

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Intelligent routing in networks has opened up many challenges in modelling and methods, over the past decade. Many techniques do exist for routing on such an environment where path determination was carried out by advertisement, position and near-optimum node selection schemes. In this paper, an efficient routing scheme has been proposed using genetic algorithm for a grade-based two-level node selection method. This method assumes that nodes have the knowledge of its environment and is capable of taking decision for route discovery. The data learnt from the topology which is under consideration for routing, is saved in its local memory. In this two-level node selection scheme, the route discovery operation takes place in multiple levels. At the first level, the grade based selection is applied for considering the most optimal nodes which would be fit for sending data. At the second level, the optimal path is discovered using Genetic Algorithm. The simulation result shows that faster convergence of path took place in the case of the proposed method with good fitness value, as compared to non-graded network.

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Application of genetic algorithm on quality graded networks for intelligent routing

Cited by 8 publications

References 5 publications

A QoS-based routing approach using genetic algorithms for bandwidth maximisation in networks

A QoS-based routing approach using genetic algorithms for bandwidth maximisation in networks

Competitive performance analysis of two evolutionary algorithms for routing optimization in graded network

An intelligent routing approach using genetic algorithms for quality graded network

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