Improved Grey Wolf‐Differential Evolution Algorithm for UAV OAM‐MDI‐QKD Parameter Optimization

• DOI: 10.1002/qute.202400423
• Publication Date: 2025-02-12T11:53:52Z
• Authors: Dan Wu; Jiahao Li; Xile Cui; Zhifeng Deng; Jie Tang; Yuexiang Cao; Ying Liu; Haoran Hu; Ya Wang; XingYu Wang; Huicun Yu; Jiahua Wei; Huazhi Lun; Lei Shi
• Keywords: Differential Evolution; Optimization algorithm

Abstract The integration of unconditional security quantum key distribution (QKD) with the flexibility unmanned aerial vehicles (UAVs) presents significant potential for deployment comprehensive networks. However, imperfect components practical QKD systems offer avenues eavesdropping, while limited payload capacity and vibrations UAVs present substantial challenges. This paper introduces an orbital angular momentum (OAM) encoding strategy combined decoy‐state measurement‐device‐independent (MDI‐QKD) protocol to address issues reference frame misalignment in UAV‐based systems. Nevertheless, communication performance OAM‐MDI‐QKD system is significantly affected by complex environmental challenges airborne channel. To improve degraded platforms, enhanced grey wolf optimization‐differential evolution (GWO‐DE) algorithm developed, utilizing chaotic mapping a nonlinear decay factor strengthen global search convergence capabilities, which effectively addresses limitations local algorithms (LSA) when dealing high‐dimensional objective functions. Simulation results demonstrate that GWO‐DE outperforms other traditional optimization terms precision transmission distance enhancement, computational speed meeting requirements.