Mission-level energy efficiency optimization for multi-UAV data collection using a genetic algorithm
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Keywords:
Multi-UAV systems, genetic algorithm, mission-level optimization, energy efficiency, data collection
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Abstract
The efficient utilization of limited onboard energy remains a fundamental challenge in cooperative multi-UAV data collection missions. Existing routing approaches typically optimize surrogate objectives, such as travel distance or aggregate energy consumption, which do not directly reflect mission effectiveness. The present paper proposes a Mission-level Energy-aware Genetic Algorithm (ME-GA) that directly maximizes mission-level energy efficiency, defined as the ratio of successfully delivered sensing data to total energy consumption. The proposed framework integrates a mission-level simulator into the fitness evaluation, explicitly modeling UAV propulsion, sensing, data buffering, wireless communication, and return-to-base feasibility under energy constraints. Extensive simulations involving up to 9 UAVs and 100 Points of Interest (PoIs) under both grid and random spatial layouts demonstrate that ME-GA consistently achieves high and stable energy efficiency while maintaining near-complete task satisfaction and high data delivery reliability. In comparison to GA-based baselines, the proposed approach enhances energy efficiency by approximately 5–15% across the evaluated scenarios along with a reduction in total travel distance by up to 40% in larger fleet sizes. Overall, the results demonstrate that mission-level energy efficiency serves as a unified and physically meaningful objective for multi-UAV optimization, enabling robust and scalable performance across diverse operational scenarios.
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Anif, M., Sulistyo, S., & Wayan, M. (2026). Mission-level energy efficiency optimization for multi-UAV data collection using a genetic algorithm. Communications in Science and Technology, 11(1), 18–30. https://doi.org/10.21924/cst.11.1.2026.1901
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References
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2. Y. Li, W. Chen, B. Fu, Z. Wu, and L. Hao, A Global Coverage Path Planning Method for Multi-UAV Maritime Surveillance in Complex Obstacle Environments, Drones. 8 (2024) 764.
3. M. Lyu, Y. Zhao, C. Huang, and H. Huang, Unmanned Aerial Vehicles for Search and Rescue: A Survey, Remote Sens. 15 (2023) 3266.
4. M. Tariq, A. Saadat, R. Ahmad, Z. Abaid, and J. J. P. C. Rodrigues, Enhanced Border Surveillance Through a Hybrid Swarm Optimization Algorithm, IEEE Sens. J. 23 (2023) 28172–28181.
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7. O. Cheikhrouhou and I. Khoufi, A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy, Comput. Sci. Rev. 40 (2021) 100369.
8. K. Bérczi, M. Mnich, and R. Vincze, Approximations for many-visits multiple traveling salesman problems, Omega. 116 (2023) 102816.
9. A. F. L. Villalba and E. C. G. L. Rotta, Clustering and heuristics algorithm for the vehicle routing problem with time windows, Int. J. Ind. Eng. Comput. 13 (2022) 165–184.
10. J. Chang, N. Dong, D. Li, W. H. Ip, and K. L. Yung, Skeleton Extraction and Greedy-Algorithm-Based Path Planning and its Application in UAV Trajectory Tracking, IEEE Trans. Aerosp. Electron. Syst. 58 (2022) 4953–4964.
11. Q. Meng, K. Chen, and Q. Qu, PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios, Drones. 8 (2024) 192.
12. J. Zheng, X. Sun, Y. Ji, and J. Wu, Research on UAV Path Planning Based on Improved ACO algorithm, 2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 2023, pp. 762–770.
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14. B. Li, Q. Li, Y. Zeng, Y. Rong, and R. Zhang, 3D Trajectory Optimization for Energy-Efficient UAV Communication: A Control Design Perspective, IEEE Trans. Wirel. Commun. 21 (2022) 4579–4593.
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17. X. Wu, Y. Yin, L. Xu, X. Wu, F. Meng, and R. Zhen, MULTI-UAV Task Allocation Based on Improved Genetic Algorithm, IEEE Access. 9 (2021) 100369–100379.
18. Y. He, K. Xiang, X. Cao, and M. Guizani, Task Scheduling and Trajectory Optimization Based on Fairness and Communication Security for Multi-UAV-MEC System, IEEE Internet Things J. 11 (2024) 30510–30523.
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25. H. Zhang, J. Luo, R. Li, M. Wei, X. Xie, and W. Wang, Adaptive Trajectory Optimization for UAV Swarms under Communication Constraints, 2025 IEEE International Conference on Unmanned Systems (ICUS), Changzhou, China, 2025, pp. 1751–1756.
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28. V. Lala et al., Channel Modelling for UAV Air-to-Ground Communication, 2024 5th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), Balaclava, Mauritius, 2024, pp. 1–5.
29. ETSI TS 138 214 - V15.9.0 - 5G; NR; Physical layer procedures for data (3GPP TS 38.214 version 15.9.0 Release 15), 2020.
30. S. Khan, M. Zeeshan, and Y. Ayaz, Implementation and analysis of MultiCode MultiCarrier Code Division Multiple Access (MC–MC CDMA) in IEEE 802.11ah for UAV Swarm communication, Phys. Commun. 42 (2020) 101159.
31. H. V. Abeywickrama, B. A. Jayawickrama, Y. He, and E. Dutkiewicz, Comprehensive Energy Consumption Model for Unmanned Aerial Vehicles, Based on Empirical Studies of Battery Performance, IEEE Access. 6 (2018) 58383–58394.
32. Y. Zeng, J. Xu, and R. Zhang, Energy Minimization for Wireless Communication With Rotary-Wing UAV, IEEE Trans. Wirel. Commun. 18 (2019) 2329–2345.
33. D. Datsko, F. Nekovar, R. Penicka, and M. Saska, Energy-Aware Multi- UAV Coverage Mission Planning With Optimal Speed of Flight, IEEE Robot. Autom. Lett. 9 (2024) 2893–2900.
34. Y. Wang, S. Fan, L. Wang, X. An, and D. Zhang, Research on Energy Efficiency Optimization and Path Planning for RIS-Assisted UAV Communication System, Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Intelligent Information Processing, Tianjin China, 2024, pp. 217–222.
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36. DJI, DJI Mavic 4 Pro Specifications. Accessed: Dec. 20, 2025. [Online]. Available: https://www.dji.com/id/mavic-4-pro/specs.
37. R. Diankov and J. Kuffner, Randomized statistical path planning, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, 2007, pp. 1–6.
38. A. Vince, A framework for the greedy algorithm, Discrete Appl. Math. 121 (2002) 247–260.
39. Y. Cao, W. Wei, Y. Bai, and H. Qiao, Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm, Clust. Comput. 22 (2019) 5175–5184.
40. A. Shinde and R. Bichkar, Genetic Algorithm Based Energy Efficient and Load Balanced Clustering Approach for WSN, 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1–6.
41. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2002) 182–197.
42. Y. Pan et al., Cooperative Trajectory Planning and Resource Allocation for UAV-Enabled Integrated Sensing and Communication Systems, IEEE Trans. Veh. Technol. 73 (2024) 6502–6516.
43. O. Liubimov, I. Turkin, V. Cheranovskiy, and L. Volobuieva, UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness, Computation. 12 (2024) 235.
44. S. Alqefari and M. E. B. Menai, Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions, Drones. 9 (2025)75.
45. S. Sharma and V. Kumar, A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future, Arch. Comput.Methods Eng. 29 (2022) 5605–5633.
46. G. E. M. Abro, Z. A. Ali, and R. J. Masood, Synergistic UAV Motion: A Comprehensive Review on Advancing Multi-Agent Coordination, ICCK Trans. Sens. Commun. Control. 1 (2024) 72–88.