Dynamic Learning Network-Distributed Exploration: Advancements in Hybrid Swarm Intelligence Algorithm for Congestion Control in Vehicular Ad-Hoc Networks
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 2 |
| Year of Publication : 2025 |
| Authors : Kiran Kumar Jajala, Reddaiah Buduri |
10.14445/23488379/IJEEE-V12I2P114
How to Cite?
Kiran Kumar Jajala, Reddaiah Buduri, "Dynamic Learning Network-Distributed Exploration: Advancements in Hybrid Swarm Intelligence Algorithm for Congestion Control in Vehicular Ad-Hoc Networks," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 2, pp. 124-136, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I2P114
Abstract:
At the Nexus of Intelligent Transportation Systems (ITS), Vehicular Ad-hoc Networks (VANETs) have become a quickly developing field, highlighting the necessity of a resilient and reliable VANET architecture to support increasing vehicle densities. This study has proposed a new technique, namely a Hybrid Swarm Intelligence Algorithm (HSIA), that mixes distributed exploration approaches rooted in hybrid swarm intelligence paradigms and dynamic learning mechanisms. Our proposed approach, which builds on the ideas of Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO), combines network-distributed communication, dynamic learning rates, and reinforcement learning approaches to improve algorithm performance and adaptability. We provide novel equations for adaptive pheromone updates with Dynamic Learning, Collective Exploration with Network-Distributed Pheromone Update and Adaptive Exploration-Exploitation Trade-off with Reinforcement Learning. Our experimental results on VANETS show that our method is more controlling and versatile than conventional ACO or ABC algorithms when striving for quicker convergence rates and higher-quality results. MATLAB simulations are used for the experimental validation of the HSIA technique, which shows improved performance over conventional ACO and ABC. Comparing HSIA to ACO, the packet delivery ratio and throughput increased significantly to 1.09 percent and 1.48 percent, respectively. Compared to ABC, HSIA showed an incredible increase in its packet delivery ratio and throughput of 15.94% and 9.87%, respectively. HSIA experienced a 17.25% lower end-to-end delay compared to ABC. On the other hand, HSIA’s end-to-end delay was 5.59% lower than that of ACO. Critical performance metrics showing this improvement include packet delivery ratio, throughput, and end-to-end delay.
Keywords:
Vehicular Ad-hoc Networks, Artificial Bee Colony Optimization, Ant Colony Optimization, Reinforcement learning, Swarm intelligence.
References:
[1] Farhan Aadil et al., “CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET,” PloS One, vol. 11, no. 5, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] M. Sri Lakshmi et al., “Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 2s, pp. 306-312, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Vinita Jindal, and Punam Bedi, “An Improved Hybrid Ant Particle Optimization (IHAPO) Algorithm for Reducing Travel Time in VANETs,” Applied Soft Computing, vol. 64, pp. 526-535, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Vijay Walunj, Diego Marcilio, and Bhaveet Nagaria, “Dynamic Congestion Control Mechanisms for Enhanced Efficiency in Vehicular Ad-Hoc Networks,” International Journal of Computer Engineering in Research Trends, vol. 11, no. 5, pp. 24-32, 2024.
[Publisher Link]
[5] Muhammad Arif et al., “Optimization of Communication in VANETs Using Fuzzy Logic and Artificial Bee Colony,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6145-6157, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Prashant G. Tandale, “VANET: Application of Mobile Communications in Road Traffic,” Proceedings of the 4th National Conference; INDIACom-2010: Computing for Nation Development, pp. 1-5, 2010.
[Google Scholar] [Publisher Link]
[7] G. Ravikumar et al., “Cloud Host Selection Using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 1s, pp. 247-253, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Kamlesh Rana, Sachin Tripathi, and Ram Shringar Raw, “VANET: Expected Delay Analysis for Location Aided Routing (LAR) Protocol,” BVICAM's International Journal of Information Technology, vol. 8, no. 2, pp. 1029-1037, 2016.
[Google Scholar] [Publisher Link]
[9] M. Kayalvizhi, and S. Geetha, “Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) Routing Protocol for Seamless Traffic Rerouting in Stochastic Vehicular Ad Hoc Network,” International Journal of Computer Networks and Applications, vol. 10, no. 6, pp. 993-1014, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Murad Khan, Ibrahim Shayea, and Joel J.P.C. Rodrigues, “Adaptive Hybrid Routing for Vehicular Ad-Hoc Networks Using Swarm Intelligence and Neural Network-Based Traffic Prediction,” International Journal of Computer Engineering in Research Trends, vol. 11, no. 7, pp. 13-23, 2024.
[Publisher Link]
[11] Dervis Karaboga, “An Idea Based on Honeybee Swarm for Numerical Optimization,” Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[Google Scholar] [Publisher Link]
[12] E.V.N. Jyothi et al., “A Graph Neural Network-Based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency,” Journal of Electrical Systems, vol. 19, no. 4, pp. 336-349, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] V.S. Saranya et al., “Real-Time Traffic Flow Optimization Using Adaptive IoT and Data Analytics: A Novel DeepStreamNet Model,” 2024 4th International Conference on Sustainable Expert Systems (ICSES), Kaski, Nepal, pp. 312-320, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] K.R. Ramkumar, K. Sakthivel, and C.S. Ravichandran, “ACBRAAM: A Content Based Routing Algorithm Using Ant Agents for MANETs,” BIJIT-BVICAM’s International Journal of Information Technology, vol. 3, no. 1, pp. 276-280, 2011.
[Google Scholar] [Publisher Link]
[15] Sadashiv Shirabur, Shivaling Hunagund, and Suresh Murgd, “VANET Based Embedded Traffic Control System,” 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, pp. 189-192, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] C. Kumuthini et al., “Ant with Artificial Bee Colony Techniques in Vehicular Ad-hoc Networks,” International Journal of Data Informatics and Intelligent Computing, vol. 2, no. 3, pp. 21-28, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ning Guo et al., “A Hybrid Ant Colony Optimization Algorithm for Multi-Compartment Vehicle Routing Problem,” Complexity, vol. 2020, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Sengathir Janakiraman, “A Hybrid Ant Colony and Artificial Bee Colony Optimization Algorithm-Based Cluster Head Selection for IoT,” Procedia Computer Science, vol. 143, pp. 360-366, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Anxiang Ma et al., “An Adaptive Hybrid Ant Colony Optimization Algorithm for the Classification Problem,” Information Technology and Control, vol. 48, no. 4, pp. 590-601, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] M. Kefayat, A. Lashkar Ara, and S.A. Nabavi Niaki, “A Hybrid of Ant Colony Optimization and Artificial Bee Colony Algorithm for Probabilistic Optimal Placement and Sizing of Distributed Energy Resources,” Energy Conversion and Management, vol. 92, pp. 149-161, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Changsheng Zhang, and Bin Zhang, “A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem,” Discrete Dynamics in Nature and Society, vol. 2014, no. 1, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Abba Suganda Girsang, Chun-Wei Tsai, and Chu-Sing Yang, “A Hybrid Ant-Bee Colony Optimization for Solving Traveling Salesman Problem with Competitive Agents,” Mobile, Ubiquitous, and Intelligent Computing, pp. 643-648, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Nan Zhao, Xianwang Lv, and Zhilu Wu, “A Hybrid Ant Colony Optimization Algorithm for Optimal Multiuser Detection in DS-UWB System,” Expert Systems with Applications, vol. 39, no. 5, pp. 5279-5285, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Hao Gao et al., “An Improved Artificial Bee Colony Algorithm with its Application,” IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 1853-1865, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] C. Nandagopal et al., “Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm,” Intelligent Automation & Soft Computing, vol. 36, no. 1, pp. 113-126, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Syed Mohd Faisal, and Taskeen Zaidi, “Implementation of ACO in VANETs with Detection of Faulty Node,” Indian Journal of Science and Technology, vol. 14, no. 19, pp. 1598-1614, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Fuhui Zhou, Thomas Lagkas, and Farhan Aadil, “Optimizing Edge Computing for Internet of Drones: A Hybrid Approach Using Deep Learning and Swarm-Based Routing,” Macaw International Journal of Advanced Research in Computer Science and Engineering, vol. 10, no. 1, pp. 64-73, 2024.
[Google Scholar] [Publisher Link]
[28] Omar Sami Oubbati, Adnan Shahid Khan, and Madhusanka Liyanage, “Blockchain-Enhanced Secure Routing in FANETs: Integrating ABC Algorithms and Neural Networks for Attack Mitigation,” Synthesis: A Multidisciplinary Research Journal, vol. 2, no. 2, pp. 1-11, 2024.
[Google Scholar] [Publisher Link]
[29] P. Suman Prakash et al., “Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs,” Journal of Sensors, vol. 2022, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]