无人机应急通信的多层次自适应河马优化算法研究

Research on Multi-level Adaptive Hippopotamus Optimization Algorithm for UAV Emergency Communication

摘要

为解决在战时或灾害等极端环境下,实现高效、低成本的应急通信网络快速部署,该文提出了一种多层次自适应河马优化算法(Multi-level Adaptive Hippopotamus Optimization Algorithm,MLA-HOA)的无人机部署方案,旨在应对战时或灾害等极端环境下的应急通信保障问题.该算法针对无人机部署中覆盖、连通与成本的多目标优化难题,引入了动态行为选择与状态调节机制,以自适应平衡全局探索与局部开发;设计了融合多种算子的自适应局部搜索与智能变异策略,增强局部优化能力与种群多样性;并采用增强的停滞处理与精英保留机制以提升收敛稳定性.实验结果表明,MLA-HOA在收敛速度与解质量上均显著优于对比算法(GA、PSO、HHO等),其部署方案在标准及大规模实例中的平均冗余度低至1.08,总成本降低10%以上,有效实现了覆盖、连通与成本的综合优化.该研究为无人机应急通信部署提供了高效解决方案,所提算法框架也为复杂约束优化问题提供了新思路.

Abstract

To achieve efficient and low-cost rapid deployment of emergency communication networks in extreme scenarios such as wartime or disaster situations,this paper proposes a Multi-level Adaptive Hippopotamus Optimization Algorithm(MLA-HOA) for UAV deployment.The algorithm addresses the multi-objective optimization challenges of coverage,connectivity,and cost in UAV deployment.It introduces a dynamic behavior selection and state adjustment mechanism to adaptively balance global exploration and local exploitation.A multi-operator fused adaptive local search and an intelligent mutation strategy are designed to enhance local optimization capability and population diversity.Furthermore,an enhanced stagnation handling and elite retention mechanism is adopted to improve convergence stability.Experimental results demonstrate that MLA-HOA significantly outperforms comparison algorithms(e.g.,GA,PSO,HHO) in both convergence speed and solution quality.The proposed deployment scheme achieves an average redundancy as low as 1.08 in both standard and large-scale instances,reducing the total cost by over 10%,effectively realizing the comprehensive optimization of coverage,connectivity,and cost.This research provides an efficient solution for UAV emergency communication deployment,and the proposed algorithm framework also offers new insights for complex constrained optimization problems.

关键词

无人机系统 / 河马优化算法 / 应急通信 / 空间覆盖 / 无人机部署

Key words

UAV systems / hippopotamus optimization algorithm(HOA) / emergency communications / space coverage / UAV deployment

[1] Sabzehali J,Badaru B,Aminikhah M,et al.Optimizing number,placement,and backhaul connectivity of multi-UAV networks[J].IEEE Internet of Things Journal,2022,9(21):21548-21560.
[2] Lyu Y,Wang Z,Zhang Y,et al.UAV coverage path planning of multiple disconnected regions based on cooperative optimization algorithms[J].IEEE Transactions on Cognitive and Developmental Systems,2024,16(5):202-211.
[3] Zhang B,Liu Y,Wang J,et al.Genetic algorithm enabled particle swarm optimization for aerial base station deployment[C]//IEEE 94th Vehicular Technology Conference,2021:1-5.
[4] Amiri M H,Mehdipour M,Yazdani M,et al.Hippopotamus optimization algorithm:a novel nature-inspired optimization algorithm[J].Scientific Reports,2024,14(1):5032,doi:10.1038/s41598-024-54910-3.
[5] Ghosh A,Das S K.A distributed greedy algorithm for connected sensor cover in dense sensor networks[C]//IEEE International Conference on Distributed Computing in Sensor Systems,2005:340-353.
[6] Wang Z,Xie H.Wireless sensor network deployment of 3D surface based on enhanced grey wolf optimizer[J].IEEE Access,2020,8:57229-57251,doi:10.1109/ACCESS.2020.2982441.
[7] Xiang H,Itagaki T.The social impact of IoT in disasters[C]//12th International Conference on Knowledge Discovery,2025,doi:10.1109/ITIKD.2025.9876543.
[8] Mozaffari M,Saad W,Bennis M,et al.A tutorial on UAVs for wireless networks:applications,challenges,and open problems[J].IEEE Communications Surveys & Tutorials,2019,21(3):2334-2360.
[9] Sobouti M J,Keshmiri S S,Nawab M A,et al.Utilizing UAVs in wireless networks:advantages,challenges,objectives,and solution methods[J].Vehicles,2024,6(4):1769-1800.
[10] Pan H,Liu C,Shen Y,et al.Resource scheduling for UAVs-aided D2D networks:a multi-objective optimization approach[J].IEEE Transactions on Wireless Communications,2023,23(5):4691-4708.
[11] Tan X,Wang Y,Li Z,et al.Learning to detect critical nodes in sparse graphs via feature importance awareness[J].IEEE Transactions on Automation Science and Engineering,2024,22(1):129-140.
[12] Guo Y,Li K,Laverty D M,et al.Synchrophasor-based islanding detection for distributed generation systems using systematic principal component analysis approaches[J].IEEE Transactions on Power Delivery,2015,30(6):2544-2552.
[13] Mahoro Ntwari D,Li J,Wang K,et al.Time efficient unmanned aircraft systems deployment in disaster scenarios using clustering methods and a set cover approach[J].Electronics,2021,10(4):422,doi:10.3390/electronics10040422.
[14] Azoulay R,Haddad Y,Reches S,et al.Machine learning methods for UAV flocks management:a survey[J].IEEE Access,2021,9:139146-139175,doi:10.1109/ACCESS.2021.3118978.
[15] He W,Qi X,Liu L.A novel hybrid particle swarm optimization for multi-UAV cooperate path planning[J].Applied Intelligence,2021,51(10):7350-7364.
[16] Liu B,Wang L,Jin Y H,et al.Improved particle swarm optimization combined with chaos[J].Chaos,Solitons & Fractals,2005,25(5):1261-1271.
[17] Xu X F,Li Y,Wang R,et al.Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration[J].Renewable Energy,2024,223:120086,doi:10.1016/j.renene.2024.120086.
[18] Xu G.An adaptive parameter tuning of particle swarm optimization algorithm[J].Applied Mathematics and Computation,2013,219(9):4560-4569.
[19] Naouri A,Wu H,Boukerche A,et al.Maximizing UAV fog deployment efficiency for critical rescue operations:a multi-objective optimization approach[J].Future Generation Computer Systems,2024,159:255-271,doi:10.1016/j.future.2024.05.007.
[20] Wan Y,Zhao W,Li S,et al.An accurate UAV 3-D path planning method for disaster emergency response based on an improved multiobjective swarm intelligence algorithm[J].IEEE Transactions on Cybernetics,2022,53(4):2658-2671.
[21] Faris H,Alajmi A,Alshatebi S H,et al.Grey wolf optimizer:a review of recent variants and applications[J].Neural Computing and Applications,2018,30(2):413-435.
[22] Alabool H M,Alarabiat D,Habib M,et al.Harris hawks optimization:a comprehensive review of recent variants and applications[J].Neural Computing and Applications,2021,33(15):8939-8980.
[23] Park M W,Kim Y D.A systematic procedure for setting parameters in simulated annealing algorithms[J].Computers & Operations Research,1998,25(3):207-217.
[24] Tarekegn G B,Munari A,Mulugeta L,et al.Deep-reinforcement-learning-based drone base station deployment for wireless communication services[J].IEEE Internet of Things Journal,2022,9(21):21899-21915.

基金资助

国家自然科学基金项目(62272239)资助.