PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Sheikhan, Mansour; Hemmati, Ehsan

doi:10.1080/18756891.2012.696921

Cited by 16 publications

(6 citation statements)

References 49 publications

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“…BIAs proved to be more efficient and autonomous than other Artificial intelligence algorithms in unknown and changing environments as MANET. BIAs as routing protocols in MANET was investigated in many proposed studies as in [38][39][40][41][42][43]. While other researchers believe that the main objective of using BIAs in MANET is to optimize nodes energy as it is one of the main factors that affect the Quality of Service (QoS) Metrics.…”

Section: Introductionmentioning

confidence: 99%

Elephant Herding Optimization Ad Hoc On-Demand Multipath Distance Vector Routing Protocol for MANET

Sarhan

2021

IEEE Access

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Energy consumption always represents a challenge in the ad hoc networks which spurred the researchers to benefit from the bio-inspired algorithms and their fitness functions to evaluate nodes energy through the path discovery stage. In this paper we propose energy efficient routing protocol based on the well-known Ad Hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol and a bio-inspired algorithm called Elephant Herding Optimization (EHO). In the proposed EHO-AOMDV the overall consumed energy of nodes is optimized by classifying nodes into two classes, while paths are discovered from the class of the fittest nodes with sufficient energy for transmission to reduce the probability of path failure and the increasing number of dead nodes through higher data loads. The EHO updating operator updates classes based on separating operator that evaluates nodes based on residual energy after each transmission round. Experiments were conducted using Ns-3 with five evaluation metrics routing overhead, packet delivery ratio, average energy consumption, end-to-end delay and number of dead nodes and four implemented protocols the proposed protocol , AOMDV and two bio-inspired protocols ACO-FDRPSO and FF-AOMDV. Results indicated that the proposed EHO-AOMDV attained higher packet delivery ratio with less routing overhead, average energy consumption and number of dead nodes over the state of art while in the end-to-end delay AOMDV has outperformed the proposed protocol.

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Section: Introductionmentioning

confidence: 99%

Elephant Herding Optimization Ad Hoc On-Demand Multipath Distance Vector Routing Protocol for MANET

Sarhan

2021

IEEE Access

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“…Moreover, the energy efficiency of the optimization approach is restrained by various factors namely residual energy of sensor node (SN), bandwidth, intra-cluster distance, harmony memory size, energy consumption, harmony memory considering rate and path length. In a multi-dimensional hunt space, the swarm particles are moved and have their own network node position vector and velocity, which are defined as follows Sheikhan and Hemmati [44]:…”

Section: Optimization Techniquementioning

confidence: 99%

“…The velocity is measured based on the below equation, which is from Shi and Eberhart [45], Sheikhan and Hemmati [44]:…”

Section: Optimization Techniquementioning

confidence: 99%

An Energy- Efficient Optimal multi-dimensional location, Key and Trust Management Based Secure Routing Protocol for Wireless Sensor Network

2021

KSII TIIS

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“…The most important factor in utilizing ANN is the determinations of its network structure and parameters. Evolutionary algorithms such as ICA 2 and PSO 3,4 can be employed to achieve these objectives.…”

Section: Introductionmentioning

confidence: 99%

Continuous Prediction of the Gas Dew Point Temperature for the Prevention of the Foaming Phenomenon in Acid Gas Removal Units Using Artificial Intelligence Models

Rohani¹,

Jazayeri-Rad²,

Behbahani³

2017

IJCIS

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Acid gas removal (AGR) units are widely used to remove CO 2 and H 2 S from sour gas streams in natural gas processing. When foaming occurs in an AGR system, the efficiency of the process extremely decreases. In this paper, a novel approach is suggested to regularly predict the gas dew point temperature (GDPT) in order to anticipate the foaming conditions. Prediction of GDPT is advantageous because the conventional methods of measuring GDPT such as: (i) using a chilled mirror device is time consuming; and (ii) the use of gas chromatograph for composition determination combined with the equation-of-state calculations involve column retention time and is expensive. New hybrid modeling algorithms based on the artificial neural network (ANN) combined with either the imperialist competitive algorithm (ICA) or particle swarm optimization (PSO) are employed to model the process. The models can then be used to prevent the foaming phenomenon. The proposed algorithms combine the local searching ability of ANN with the global searching abilities of ICA and PSO. ICA and PSO are used to optimize the initial weights of the neural networks. The resulting ICA-ANN and PSO-ANN combined algorithms are then applied to model the occurrence of foaming in the AGR plant based on a simulation data set acquired from the 6 th refinery of the south Pars gas complex in Iran. The performances of the ICA-ANN, PSO-ANN and conventional ANN models are then compared against each other. It was found that the accuracies of the ICA-ANN and PSO-ANN models are better than that of the conventional ANN model. In addition, the PSO-ANN model outperformed the ICA-ANN model.

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PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Cited by 16 publications

References 49 publications

Elephant Herding Optimization Ad Hoc On-Demand Multipath Distance Vector Routing Protocol for MANET

Elephant Herding Optimization Ad Hoc On-Demand Multipath Distance Vector Routing Protocol for MANET

An Energy- Efficient Optimal multi-dimensional location, Key and Trust Management Based Secure Routing Protocol for Wireless Sensor Network

Continuous Prediction of the Gas Dew Point Temperature for the Prevention of the Foaming Phenomenon in Acid Gas Removal Units Using Artificial Intelligence Models

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