Report on optimisation for efficient dynamic task distribution in drone swarms using QRDPSO algorithm
Converso G. Mehiar D. Rukovich A. Brzhanov R.
February 2025Ain Shams University
Ain Shams Engineering Journal
2025#16Issue 2
The primary aim was to develop a Quantum Robot Darwinian Particle Swarm Optimisation (QRDPSO) algorithm and assess its performance against the conventional RDPSO. Using MATLAB-based mathematical modelling, QRDPSO was evaluated for its efficiency in dynamic task distribution and inter-drone communication stability. The results demonstrate that QRDPSO finds optimal solutions 16.3% faster than RDPSO, with performance improvements as the swarm size increases. Specifically, when the number of drones was increased from 5 to 20, the number of iterations required for QRDPSO changed from 384 to 189. However, for RDPSO, the number of iterations changed from 439 to 242. Additionally, QRDPSO showed a 27.1% reduction in drone loss rates, outperforming RDPSO in terms of maintaining operational resources, especially in larger swarms. These findings have practical implications, as QRDPSOs efficiency and stability can support extensive drone applications requiring synchronised, reliable swarm behaviour.
Algorithm , Darwinian , QRDPSO , Quantum-optimised , RDPSO , Swarm
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DICMAPI - Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, Italy
Faculty of Information Technology, Artificial Intelligence Department, Middle East University (MEU), Amman, Jordan
Technical Institute (branch) of the State Autonomous Educational Institution of Higher Professional Education North-Eastern Federal Institute of MK Ammosova in Neryungri, Neryungri, Russian Federation
Department of Construction Engineering, Caspian University of Technology and Engineering named after Sh. Yessenov, Aktau, Kazakhstan
DICMAPI - Department of Chemical
Faculty of Information Technology
Technical Institute (branch) of the State Autonomous Educational Institution of Higher Professional Education North-Eastern Federal Institute of MK Ammosova in Neryungri
Department of Construction Engineering
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