Optimizing MANET routing in AODV: An hybridization approach of ACO and firefly algorithm

Nath, Subhrapratim; Banik, Samriddha; Seal, Amab; Sarkar, Subir Kumar

doi:10.1109/icrcicn.2016.7813643

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…For optimization, the flashing light is considered as an objective function. 44 In Equation 2, using inverse square law, the I at an exact distance (r) from a light source is determined.…”

Section: Ff Optimization Algorithmmentioning

confidence: 99%

“…The description of the methods that are taken for comparison is discussed in the below paragraphs: AODV 44 : AODV is a loop-free ad hoc routing protocol. It is built to be self-starting in a mobile node setting, withstanding a variety of network behaviours, including node instability, connection failures and packet loss.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Moreover, light is being absorbed by the air, and due to this, there is a decreased level of intensity which turn out to be weaker when the distance increases. For optimization, the flashing light is considered as an objective function 44 . In Equation , using inverse square law, the I at an exact distance ( r ) from a light source is determined.…”

Section: Proposed Geographic Routing With Hybrid Ffgso Algorithmmentioning

confidence: 99%

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Geographic routing with hybrid firefly algorithm and galactic swarm optimization for efficient ‘void’ handling in mobile ad hoc networks

Kaliyamurthi

Anandhakumar

2020

Int J Communication

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Summary Geographic routing in mobile ad hoc network (MANET) has gained much attention even though it fails to find a route when a node meets a void zone (hole). Therefore, an efficient approach is required to avoid the void issues and to effectively transfer the data packets if a path is available between the source and destination nodes. For such, a hybrid evolutionary algorithm is introduced with geographical routing protocol (GRP) for avoiding the void, which selects the shortest route for routing from the source to the destination by combining firefly (FF) algorithm and galactic swarm optimization (HFFGSO). This proposed algorithm exploits the strengths of both the FF algorithm and galactic swarm mechanisms. In this paper, the cross‐layer design approach is implemented, which contains data link and network layer. The proposed hybrid FFGSO‐GRP algorithm improves the routing process within these layers. The implementation process for this proposed technique is carried out in NS2 simulator. From the simulation results, it is observed that our hybrid protocol when compared with the existing approaches proves its significance in terms of packet delivery ratio (PDR), delay, throughput, energy efficiency, power consumption, routing overhead and hop count.

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“…For optimization, the flashing light is considered as an objective function. 44 In Equation 2, using inverse square law, the I at an exact distance (r) from a light source is determined.…”

Section: Ff Optimization Algorithmmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

Section: Proposed Geographic Routing With Hybrid Ffgso Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Geographic routing with hybrid firefly algorithm and galactic swarm optimization for efficient ‘void’ handling in mobile ad hoc networks

Kaliyamurthi

Anandhakumar

2020

Int J Communication

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show abstract

“…The downside existing in this approach is that the time taken for determining the routing path and the distance of the routing path remains high when compared to the regular ACO algorithm. In the literature [ 25 ], by combining the firefly algorithm with ACO, the firefly algorithm is used to obtain the list of the leader nodes, the distance between the nodes, and the density of the nodes. The feature of the firefly algorithm is that the optional set of the next hop is the area with a large density of nodes.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the literature [ 17 , 24 , 25 ] just improved the convergence speed of the algorithm, while the literatures [ 20 , 22 , 23 ] only improved the energy aspect of the node. Aiming at these problems, in this paper, from the perspective of topology-based routing protocols, considering the mobility and energy-constrained features of nodes, the ant colony optimization algorithm is used to design routing protocols.…”

Section: Introductionmentioning

confidence: 99%

Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks

Yang

Xia

et al. 2018

Sensors

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The mobile ad hoc network (MANET) is a multi-hop, non-central network composed of mobile terminals with self-organizing features. Aiming at the problem of extra energy consumption caused by node motion in MANETs, this paper proposes an improved energy and mobility ant colony optimization (IEMACO) routing algorithm. Firstly, the algorithm accelerates the convergence speed of the routing algorithm and reduces the number of route discovery packets by introducing an offset coefficient of the transition probability. Then, based on the energy consumption rate, the remaining lifetime of nodes (RLTn) is considered. The position and velocity information predicts the remaining lifetime of the link (RLTl). The algorithm combines RLTn and RLTl to design the pheromone generation method, which selects the better quality path according to the transition probability to ensure continuous data transmission. As a result, the energy consumption in the network is balanced. The simulation results show that compared to the Ad Hoc on-demand multipath distance vector (AOMDV) algorithm with multipath routing and the Ant Hoc Max-Min-Path (AntHocMMP) algorithm in consideration of node energy consumption and mobility, the IEMACO algorithm can reduce the frequency of route discovery and has lower end-to-end delay as well as packet loss rate especially when nodes move, and can extend the network lifetime.

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A Review of Nature-Inspired Algorithm-Based Multi-objective Routing Protocols

Kaushik

Singh

Kumari

2021

Intelligent Learning for Computer Vision

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Optimizing MANET routing in AODV: An hybridization approach of ACO and firefly algorithm

Cited by 12 publications

References 9 publications

Geographic routing with hybrid firefly algorithm and galactic swarm optimization for efficient ‘void’ handling in mobile ad hoc networks

Geographic routing with hybrid firefly algorithm and galactic swarm optimization for efficient ‘void’ handling in mobile ad hoc networks

Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks

A Review of Nature-Inspired Algorithm-Based Multi-objective Routing Protocols

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