Enhanced Artificial Bee Colony Algorithm with Adaptive Position Update and Adaptive Exploration: Exploiting Optimization for Congestion Control in Vehicular Ad-Hoc Networks

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : Kiran Kumar Jajala, Reddaiah Buduri

10.14445/23488549/IJECE-V11I7P103

How to Cite?

Kiran Kumar Jajala, Reddaiah Buduri, "Enhanced Artificial Bee Colony Algorithm with Adaptive Position Update and Adaptive Exploration: Exploiting Optimization for Congestion Control in Vehicular Ad-Hoc Networks," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 7, pp. 20-36, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I7P103

Abstract:

A subset of Mobile Ad-hoc Networks (MANETs) consisting of Roadside Units (RSUs) and vehicles as wireless nodes are called Vehicular Ad-hoc Networks (VANETs). The basic idea is to allow vehicles to communicate among themselves about the movement of vehicles and operations, such as speed, location, acceleration, and deceleration, to roadside units. RSUs are placed along the roadside and are used by vehicles to communicate with one another. Every new vehicle has an On-Board Unit (OBU) installed. Communication between vehicles and between vehicles and infrastructure is crucial for several reasons, including public safety, passenger comfort, and road safety. A well-known method of artificial bee colony is the evolutionary algorithm having good performance in exploration although not in exploitation. This paper presents an Enhanced Artificial Bee Colony Algorithm with Adaptive Position Update and Adaptive Exploration-Exploiting (EABC-APUAEE), a novel strategy that improves the performance of the Artificial Bee Colony (ABC) algorithm. This combines two crucial adaptive mechanisms: the Adaptive Position Update (APU) and the Adaptive Exploration-Exploiting (AEE) optimization. The APU modifies bee locations to make the solution converge more quickly, while AEE optimization manages to maintain both exploration and exploitation. According to simulation results, the method which is proposed outperforms the original artificial bee colony algorithm by means of high packet delivery ratio, throughput, and decreased end-to-end delay. The simulation findings show that an enhanced artificial bee colony method with adaptive strategies can attain optimal routes more successfully. The performance is analyzed using MATLAB. The packet delivery ratio and throughput of EABC-APUAEE showed a significant increase to 30.78% and 37.21% when compared to ABC. In comparison to ABC end-to-end delay of EABC-APUAEE decreased to 50.54%.

Keywords:

Vehicular ad-hoc networks, Adaptive mechanism, Artificial bee colony, Roadside unit, Onboard unit.

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