Abstract
This study focuses on optimizing swarm communication and firefighting efficiency by integrating Reinforcement Learning (RL) with Particle Swarm Optimization (PSO). The purpose of this research is to develop an intelligent drone swarm system capable of performing dynamic tasks in complex environments such as firefighting operations. The problem addressed involves optimizing communication reliability and task allocation within a drone swarm to enhance firefighting effectiveness. The system utilizes an RL-PSO framework to balance swarm coordination and real-time adaptability while minimizing energy consumption and maximizing operational efficiency. In this approach, the RL algorithm enables drones to adapt and learn optimal actions based on rewards and penalties, while PSO guides the swarm toward optimal collective behavior by sharing positional and velocity updates. Simulation results demonstrate that the RL-PSO integration improves task execution efficiency and energy management. Quantitative results show a peak cohesion force of 0.79 at t = 49s, demonstrating the swarm’s ability to maintain formation. The RL algorithm successfully improved decision-making over 50 episodes, with Q-values increasing from 0.1 to 1.8, indicating an enhancement in swarm coordination. Furthermore, the study evaluates wireless communication strength between drones, showing a significant reduction in signal strength as distance increases, with values of 0.01dB at 10 meters and 0.0004dB at 50 meters. These findings emphasize the importance of maintaining proximity for effective communication. The hybrid RL-PSO framework significantly enhances drone swarm performance, providing a scalable solution for real-time applications in disaster management, precision agriculture, and other complex operations. This research highlights the potential of RL-PSO-based drone systems to optimize communication, coordination, and efficiency in swarm operations, pushing the boundaries of autonomous systems in firefighting and other critical applications.
Keywords
RL-PSO, Flocking Algorithm, Communication, Coordination, Firefighting, Dynamic Environment.
Citations
IRE Journals:
O. D. Ologiba, B. I. Bakare, S. Orike "Optimizing Swarm Communication and Firefighting Efficiency Using an RL-PSO Framework" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 1073-1083 https://doi.org/10.64388/IREV9I10-1715944
IEEE:
O. D. Ologiba, B. I. Bakare, S. Orike
"Optimizing Swarm Communication and Firefighting Efficiency Using an RL-PSO Framework" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715944
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