A Review Paper on Different Application of Genetic Algorithm for Mobile Ad-hoc Network (MANET)

Shi, Jinfa; Habib, Misbah; Yan, Hai

doi:10.3991/ijoe.v16i05.13325

Cited by 6 publications

(2 citation statements)

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“…• GA: Through the application of a series of mutation, crossover, and fitness assessments to several chromosomes, the GA addresses optimization problems. This method starts with a population with many chromosomes [57], [58], after that simulates natural selection to see which species could adapt to changes in their environment and reproduce and carry on to the next generation [59], where each one is representing the problem's optimal solution, as determined via an objective function [35].…”

Section: 3mentioning

confidence: 99%

Artificial Neural Network Hyperparameters Optimization: A Survey

Kadhim¹,

Abdullah²,

Ghathwan³

2022

Int. J. Onl. Eng.

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Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.

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Section: 3mentioning

confidence: 99%

Artificial Neural Network Hyperparameters Optimization: A Survey

Kadhim¹,

Abdullah²,

Ghathwan³

2022

Int. J. Onl. Eng.

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“…The RSU-based routing protocols are responsible for relaying data traffic to remote vehicles in sparse or detached networks in the CIoVs. Many existing routing protocols struggle to attain a high packet delivery ratio (PDR) within stringent time constraints, primarily due to significant link disruptions, dynamic changes in topology [5], and the rapid movement of vehicles. This presents a significant challenge for researchers when routing data packets within networks [6].…”

Section: Introductionmentioning

confidence: 99%

Mobile Adaptive Routing Algorithm for Road-Aware Infrastructure-Assisted Communication in Cognitive Internet of Vehicles

Divyashree M,

Rangaraju H G,

Revanna C R

2024

Int. J. Interact. Mob. Technol.

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The Internet of Things (IoT) is expanding the capabilities of traditional vehicular ad-hoc networks (VANET) into the Internet of Vehicles (IoV). However, there are a few challenges that need to be addressed in order to enhance the intelligence of IoV, leading to the development of a new evolving technology called Cognitive Internet of Vehicles (CIoV). In this study, we propose a road-aware infrastructure-assisted and vehicle-to-vehicle (V2V) adaptive routing system to select the most efficient route for delivering data from the source to the destination vehicle, with the aim of reducing data delivery delay. The performance is measured and evaluated based on packet delivery ratio (PDR), average end-to-end (E2E) delay, and normalised routing overhead using MATLAB. By comparing the proposed mobile adaptive routing algorithm (MARA) with existing protocols, it has been examined and found to outperform the existing ones in terms of performance.

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