مسیریابی برای شبکه‌ای از پهپادها با هدف جست‌وجو و نجات

چکیده مقاله :

مسیریابی شبکه‌ای پهپادها برای عملیات جست‌وجو و نجات یک چالش مهم است. این چالش به‌دلیل محدودیت‌های فیزیکی پهپادها، شرایط محیطی نامساعد، و محدودیت‌های زمانی است. در این مقاله، یک روش جدید برای مسیریابی شبکه‌ای پهپادها با استفاده از الگوریتم Q-Learning ارائه شده‌است. این الگوریتم به پهپادها امکان می‌دهد تا به‌صورت خودکار بهترین مسیرها را در محیط‌های پیچیده ترسیم کنند و همچنین با تغییرات محیطی سازگار شوند. نتایج شبیه‌سازی‌های انجام شده نشان‌می‌دهد که الگوریتم Q-Learning می‌تواند مسیرهای کوتاه‌تر و کارآمدتری را نسبت به الگوریتم‌های حوزه ژنتیک پیدا کند. این نتایج نشان‌می‌دهد که الگوریتم Q-Learning می‌تواند یک روش امیدوارکننده برای بهبود مسیریابی شبکه‌ای پهپادها در عملیات جست‌وجو و نجات باشد.

چکیده انگلیسی :

Network routing of drones for search and rescue operations is a critical challenge. This challenge arises due to the physical limitations of drones, adverse environmental conditions, and time constraints. In this paper, a novel approach for network routing of drones using the Q-Learning algorithm is proposed. This algorithm enables drones to automatically determine optimal paths in complex environments and adapt to environmental changes. Simulation results demonstrate that the Q-Learning algorithm can find shorter and more efficient routes compared to genetic algorithms. These findings highlight Q-Learning as a promising method for improving network routing of drones in search and rescue operations

منابع و مأخذ:

1.Mirjalili, Seyedali, and Seyed Mohammad Mirjalili. "A comparative study of the ant lion optimizer and its variants for global optimization problems." Applied Intelligence 48.8 (2018): 2450-2477.
2. Dasgupta, S., and A. Roy. Recent advances in swarm intelligence and evolutionary computation: Theory and applications. Springer, 2016.
3. Das, S., S. S. Mullick, and B. K. Roy. "An efficient hybrid algorithm based on ant colony optimization and differential evolution for global optimization problems." Applied Soft Computing 53 (2017): 170-188.
4. Wang, G. G., S. Deb, and L. D. Coelho. "Hybridizing antlion optimizer with differential evolution for constrained numerical and engineering optimization problems." Neural Computing and Applications 31.5 (2019): 1455-1493.
5. Rashedi, E., H. Nezamabadi-pour, and S. Saryazdi. "GSA: a gravitational search algorithm." Information sciences 179.13 (2009): 2232-2248.
6. Zheng, J., Y. Liang, and B. Qu. "An enhanced ant colony optimization algorithm for solving continuous optimization problems." Applied mathematics and computation 181.2 (2006): 915-927.
7. Mallipeddi, R., P. N. Suganthan, and Q. K. Pan. "Differential evolution algorithm with ensemble of parameters and mutation strategies." Applied Soft Computing 10.2 (2010): 520-532.
8. Wang, G. G., and S. Deb. "Multi-objective ant lion optimizer for unconstrained and constrained optimization problems." Applied Soft Computing 77 (2019): 272-308.
9.Rahnamayan, S., H. R. Tizhoosh, and M. M. A. Salama. "Opposition-based differential evolution." IEEE transactions on evolutionary computation 12.1 (2008): 64-79.
10. Liu, B., L. Wang, and X. Zhang. "A hybrid artificial bee colony algorithm with modified search mechanism for global optimization." Computers & Operations Research 46 (2014): 29-39.
11.Wang, G. G., and S. Deb. "Antlion optimizer with crossover operation for numerical and engineering optimization problems." International Journal of Machine Learning and Cybernetics 10.5 (2019): 1053-1080.
12.Mirjalili, S., and A. Lewis. "The whale optimization algorithm." Advances in engineering software 95 (2016): 51-67.
13.Kennedy, J., and R. C. Eberhart. "Particle swarm optimization." Proceedings of IEEE international conference on neural networks (1995): 1942-1948.
14.Shi, Y., and R. C. Eberhart. "A modified particle swarm optimizer." Evolutionary Computation Proceedings (1998): 69-73.
15.Liang, J. J., A. K. Qin, P. N. Suganthan, and S. Baskar. "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions." IEEE Transactions on Evolutionary Computation 10.3 (2006): 281-295.
16.Das, S., and P. N. Suganthan. "Differential evolution: a survey of the state-of-the-art." IEEE transactions on evolutionary computation 15.1 (2011): 4-31.
17.Das, S., S. Biswas, and A. Roy. "A review of differential evolution algorithm and its variants for global optimization." Swarm and Evolutionary Computation 34 (2017): 1-22.
18.Das, S., and A. Abraham. "Differential evolution: a survey of the state-of-the-art." IEEE transactions on evolutionary computation 15.1 (2011): 4-31.
19.Das, S., & Mullick, S. S. (2019). A comprehensive survey of ant colony optimization algorithms and their applications. Swarm and Evolutionary Computation, 46, 54-89.
20.Hosseini, S. H., & Farsangi, M. A. (2018). An efficient hybrid algorithm based on particle swarm optimization and differential evolution. Journal of Computational Science, 28, 25-37.
21.Sonny, A., Yeduri, S. R., & Cenkeramaddi, L. R. (2023). Autonomous UAV Path Planning using Modified PSO for UAV-Assisted Wireless Networks. IEEE Access.
22.Din, A. F. U., et al. (2023). Robust flight control system design of a fixed wing UAV using optimal dynamic programming. Soft Computing, 27(6), 3053-3064.
23.Salameh, H. B., Alhafnawi, M., Masadeh, A. E., & Jararweh, Y. (2023). Federated reinforcement learning approach for detecting uncertain deceptive target
using autonomous dual UAV system. Information Processing & Management, 60(2), 103149.
24.Wu, J., et al. (2023). An Adaptive Conversion Speed Q-Learning Algorithm for Search and Rescue UAV Path Planning in Unknown Environments. IEEE Transactions on Vehicular Technology.
25.Parvaresh, N., & Kantarci, B. (2023). A Continuous Actor-Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities. IEEE Open Journal of the Communications Society, 4, 700-712.
26.Muy, S., & Lee, J.-R. (2023). Joint optimization of trajectory, beamforming, and power allocation in UAV-enabled WPT networks using DRL combined with water-filling algorithm. Vehicular Communications, 43, 100632.
27.Li, B., et al. (2023). Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach. IEEE Internet of Things Journal.
28.Trotti, F., Farinelli, A., & Muradore, R. (2023). An online path planner based on POMDP for UAVs. In 2023 European Control Conference (ECC) (pp. 1471-1476). IEEE.
29.Chen, P., Li, H., & Ma, L. (2023). Distributed massive UAV jamming optimization algorithm with artificial bee colony. IET Communications, 17(2), 197-206.
30.Khashan, M. K., Khudhur, D. S., & Balla, H. H. (2023). Comparison between the two methods of optimization: Genetic algorithm (GA) and ant colony algorithm (ACO) for the propulsion system of UAV