Citation

Maragatharajan, M. and Sureshkumar, Aanjankumar and Dhanaraj, Rajesh Kumar and Nirmala, E. and Sayeed, Md Shohel and Quasim, Mohammad Tabrez and Basheer, Shakila (2025) Hybrid Feed Forward Neural Networks and Particle Swarm Optimization for Intelligent Self-Organization in the Industrial Communication Networks. IEEE Open Journal of the Communications Society. p. 1. ISSN 2644-125X

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Abstract

In recent times, the Industrial Communication Networks (ICNets) have been playing a vital role in advancing mobile generation networks, especially in the evolution of 6G networks. This research proposes a novel technique for self-organization that integrates Feed Forward Neural Network (FFNN) and Particle Swarm Optimization (PSO) to enhance the network management, optimization and adaptive learning in 6G. The traditional self-organization models in ICNets and 6G depends on rule-based heuristic, reinforcement learning and classical optimization techniques, which often struggle with high computational complexity, slow convergence rates, and suboptimal decision making. In contrast, FFNN+PSO fusion model leverages the predictive learning capability of FFNN and the global optimization strength of PSO to ensure intelligent self-optimization, real-time adaptability, and ultra-low-latency in the dynamically changing 6G environments. The experimental results demonstrate that the proposed method achieves a significantly higher accuracy of 98.25% by outperforming the existing models such as Random Forest (80%), Reinforcement learning (90%), Max Overlapping (88%), and Ant Colony Optimization (92%), Further, the proposed method enhances the energy efficiency, complex network function approximation, and collaborative optimization which make it an ideal choice for scalable and self-organization model in the 6G and ICNets. This study provides a transformative contribution to self-organization in the 6G networks and it offers robust, high-performance alternative to the conventional models as well it ensures massive device connectivity with intelligent network adaptation.

Item Type:	Article
Uncontrolled Keywords:	Self-organization, Particle swarm optimization, Feed forward neural networks, Activation function, Bandwidth, Fitness evaluation.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Apr 2025 02:28
Last Modified:	30 Apr 2025 02:28
URII:	http://shdl.mmu.edu.my/id/eprint/13715

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