Skip to main content

Advertisement

Springer Nature Link
Log in

An adaptive local search-based arithmetic optimization algorithm for unmanned aerial vehicle placement

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

A mobile ad hoc network (MANET) comprises multiple autonomous unmanned aerial vehicles (UAVs) connected in an ad hoc manner. MANETs are key components in achieving different services in smart cities. One of the main issues in the MANET is UAV placement, which refers to finding the optimal positions of UAVs. Recently, researchers proposed several machine-learning methods for UAV placement. The existing techniques obtained promising results; however, their performance is far from the best, and more effort is needed. This paper introduces an adaptive local search-based arithmetic optimization (LSAO) algorithm for UAV placement. The incentive mechanism of LSAO is enhancing the search dynamics by embedding an adaptive switching probability, a chaotic local search, and an opposition-based learning strategy into the standard AO algorithm. The proposed method is benchmarked on well-known placement test cases, and the results are verified by a comparative study with state-of-the-art algorithms. The results confirm that LSAO generated competitive outcomes compared to its peers in most simulation benchmarks. The LSAO obtained the first rank in terms of coverage, connectivity, and total fitness values among comparison algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Algorithm 2
Algorithm 3
Algorithm 4
Algorithm 5
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

There is no data for distribution.

Code availability

The source codes are available upon request.

References

  1. Saadi AA, Soukne A, Meraihi Y, Gabis AB, Ramdane-Cherif A (2023) A hybrid improved manta ray foraging optimization with Tabu search algorithm for solving the UAV placement problem in smart cities. IEEE Access 11:24315–24342

    Article  Google Scholar 

  2. Agrawal R et al (2023) Classification and comparison of ad hoc networks: a review. Egypt Inform J 24(1):1–25

    Article  Google Scholar 

  3. Won J, Kim DY, Park YI, Lee JW (2023) A survey on UAV placement and trajectory optimization in communication networks: from the perspective of air-to-ground channel models. ICT Express 9(3):385–397

    Article  Google Scholar 

  4. Sobouti MJ, Mohajerzadeh A, Adarbah HY, Rahimi Z, Ahmadi H (2024) Utilizing UAVs in wireless networks: advantages, challenges, objectives, and solution methods. Vehicles 6(4):1769–1800

    Article  Google Scholar 

  5. Emami H (2020) Seasons optimization algorithm. Eng Computers 38:1845–1865

    Article  Google Scholar 

  6. Wang H, Huo D, Alidaee B (2014) Position unmanned aerial vehicles in the mobile Ad Hoc network. J Intell Robot Syst 74:455–464

    Article  Google Scholar 

  7. Rahman S, Kim G, Cho Y, Khan A (2018) Positioning of UAVs for throughput maximization in software-defined disaster area UAV communication networks. J Commun Netw 20(5):452–463

    Article  Google Scholar 

  8. Al-Turjman F, Lemayian JP, Alturjman S, Mostarda L (2019) Enhanced deployment strategy for the 5G Drone-BS using artificial intelligence. IEEE Access 7:75999–76008

    Article  Google Scholar 

  9. Strumberger I, Bacanin N, Tomic S, Beko M, Tuba M (2017) Static drone placement by elephant herding optimization algorithm. 25th telecommunications forum TELFOR 2017. Serbia, Belgrade, pp 1–4

    Google Scholar 

  10. Reina DG, Tawfik H, Toral SL (2018) Multi-subpopulation evolutionary algorithms for coverage deployment of UAV-networks. Ad Hoc Netw 68:16–32

    Article  Google Scholar 

  11. Gupta M, Varma S (2021) Optimal placement of UAVs of an aerial mesh network in an emergency situation. J Ambient Intell Human Comput 12(1):343–358

    Article  Google Scholar 

  12. Chaalal E, Reynaud L, Senouci SM (2020) A social spider optimisation algorithm for 3D unmanned aerial base stations placement, IFIP networking 2020 conference and workshops. Networking 2020:544–548

    Google Scholar 

  13. Gupta M, Varma S (2021) Metaheuristic-based optimal 3D positioning of UAVs forming aerial mesh network to provide emergency communication services. IET Commun 15(10):1297–1314

    Article  Google Scholar 

  14. Ozdag R (2022) Multi-metric optimization with a new metaheuristic approach developed for 3D deployment of multiple drone-BSs. Peer-to-Peer Network Appl 15(3):1535–1561

    Article  Google Scholar 

  15. Ouamri MA, Oteşteanu ME, Barb G, Gueguen C (2022) Coverage analysis and efficient placement of drone-BSs in 5G networks. Eng Proceed 14(1):18

    Google Scholar 

  16. Mahajan P, Kumar A, Chalapathi GSS, Buyya R (2022) “EFTA: an energy-efficient, fault-tolerant, and area-optimized UAV placement scheme for search operations,” in INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops, IEEE, pp 1-6

  17. Huang H, Savkin AV (2018) An algorithm of efficient proactive placement of autonomous drones for maximum coverage in cellular networks. IEEE Wirel Commun Lett 7(6):994–997

    Article  Google Scholar 

  18. Sawalmeh A, Othman NS, Shakhatreh H (2018) Efficient deployment of multi-uavs in massively crowded events. Sensors 18(11):1–25

    Article  Google Scholar 

  19. Yang L, Yao H, Wang J, Jiang C, Benslimane A, Liu Y (2020) Multi-UAV-enabled load-balance mobile-edge computing for IoT networks. IEEE Internet Things J 7(8):6898–6908

    Article  Google Scholar 

  20. Hadiwardoyo SA et al (2020) Three dimensional UAV positioning for dynamic UAV-to-car communications. Sensors 20(2):1–18

    Article  Google Scholar 

  21. Mayor V, Estepa R, Estepa A, Madinabeitia G (2020) Energy-efficient UAVs deployment for QoS-guaranteed VoWiFi service. Sensors 20(16):1–32

    Article  Google Scholar 

  22. Zamani A, Kämmer R, Hu Y, Schmeink A (2020) Optimization of unmanned aerial vehicle augmented ultra-dense networks. Eurasip J Wirel Commun Netw 1:2020

    Google Scholar 

  23. Zhang X, Duan L (2020) Energy-saving deployment algorithms of UAV swarm for sustainable wireless coverage. IEEE Trans Veh Technol 69(9):10320–10335

    Article  Google Scholar 

  24. Shakoor S, Kaleem Di Z, Do T, Dobre OA, Jamalipour A (2021) Joint optimization of UAV 3-D placement and path-loss factor for energy-efficient maximal coverage. IEEE Internet Things J 8(12):9776–9786

    Article  Google Scholar 

  25. Munawar HS, Hammad AWA, Waller ST (2022) Disaster region coverage using drones: maximum area coverage and minimum resource utilisation. Drones 6(4):1–28

    Article  Google Scholar 

  26. Zhou Y, Dorismond J (2024) Optimal placement of UAVs to provide surveillance coverage for a ground vehicle in a collaborative search-and-rescue operation. AI, Computer Sci Robot Technol 3:1–26

    Google Scholar 

  27. Sawalmeh A, Othman NS, Liu G, Khreishah A, Alenezi A, Alanazi A (2022) Power-efficient wireless coverage using minimum number of UAVs. Sensors 22(1):1–27

    Google Scholar 

  28. Mayor V, Estepa R, Estepa A (2022) QoS-aware multilayer UAV deployment to provide VoWiFi service over 5G networks. Wirel Commun Mobile Comput 2022(1):1–13

    Article  Google Scholar 

  29. Wang L, Zhang H, Guo S, Yuan D (2022) 3D UAV deployment in multi-UAV networks with statistical user position information. IEEE Commun Lett 26(6):1363–1367

    Article  Google Scholar 

  30. Chen X, Tang W, Yang X, Zhou L, Li L (2022) PSO-VFA: a hybrid intelligent algorithm for coverage optimization of UAV-mounted base stations. J Internet Technol 23(3):487–495

    Article  Google Scholar 

  31. Wei W, Wang J, Fang Z, Chen J, Ren Y, Dong Y (2023) 3U: joint design of UAV-USV-UUV networks for cooperative target hunting. IEEE Trans Veh Technol 72(3):4085–4090

    Article  Google Scholar 

  32. Masroor R, Naeem M, Akram T, Almasoud AM, Ejaz W (2023) Optimal stratified placement of balloons and UAVs to support users’ coverage. Internet Things 23:100865

    Article  Google Scholar 

  33. Zhao Z et al (2022) Smart unmanned aerial vehicles as base stations placement to improve the mobile network operations. Computer Commun 181:45–57

    Article  Google Scholar 

  34. Ahmed Z, Ahmad A, Altaf M, Khan FA (2023) Power efficient UAV placement and resource allocation for adaptive video streaming in wireless networks. Ad Hoc Netw 150:103260

    Article  Google Scholar 

  35. Bandari S, Nirmala Devi L (2024) “A multi-objective approach for optimal target coverage UAV placement: hybrid heuristic formulation,” J Control Decis, pp 1-17

  36. Seyyedabbasi A, Aliyev R, Kiani F, Gulle MU, Basyildiz H, Shah MA (2021) Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems. Knowl-Based Syst 223:107044

    Article  Google Scholar 

  37. Emami H (2019) Chaotic election algorithm. Comput Inform 38:1444–1478

    Article  MathSciNet  Google Scholar 

  38. Yang X (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Comput 2(2):78–84

    Article  Google Scholar 

  39. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  40. Mirjalili S, Mohammad S, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  41. Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300

    Article  Google Scholar 

  42. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18

    Article  Google Scholar 

  43. Alzenad M, El-Keyi A, Lagum F, Yanikomeroglu H (2017) 3-D placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage. IEEE Wirel Commun Lett 6(4):434–437

    Article  Google Scholar 

  44. Qiu C, Wei Z, Feng Z, Zhang P (2019) Joint resource allocation, placement and user association of multiple UAV-mounted base stations with in-band wireless backhaul. IEEE Wirel Commun Lett 8(6):1575–1578

    Article  Google Scholar 

  45. Namvar N, Homaifar A, Karimoddini ALI, Maham B (2019) Heterogeneous UAV cells: an effective resource allocation scheme for maximum coverage performance. IEEE Access 7:164708–164719

    Article  Google Scholar 

  46. Wang Y, Tang Z, Member S, Huang A, Zhang H (2024) Placement of UAV-mounted edge servers for internet of vehicles. IEEE Trans Veh Technol 73(7):10587–10601

    Article  Google Scholar 

  47. Zou C, Li X, Liu X, Zhang M (2021) 3D placement of unmanned aerial vehicles and partially overlapped channel assignment for throughput maximization. Digital Commun Netw 7(2):214–222

    Article  Google Scholar 

  48. Zhang C, Zhang L, Zhu L, Zhang T, Xiao Z, Xia XG (2021) 3D deployment of multiple UAV-mounted base stations for UAV communications. IEEE Trans Commun 69(4):2473–2488

    Article  Google Scholar 

  49. Almeida EN, Campos R, Ricardo M (2022) Traffic-aware UAV placement using a generalizable deep reinforcement learning methodology, in 2022 IEEE Symposium on Computers and Communications (ISCC), IEEE, pp 1–6

  50. Almeida EN, Campos R, Ricardo M (2022) Traffic-aware UAV placement using a generalizable deep reinforcement learning methodology, in 2022 IEEE Symposium on Computers and Communications (ISCC), IEEE, pp 1-6

  51. Oliveira F, Luís M, Sargento S (2021) Machine learning for the dynamic positioning of uavs for extended connectivity. Sensors 21(13):4618

    Article  Google Scholar 

  52. Wang Y, Member S, Farooq J (2023) Deep reinforcement learning based placement for integrated access backhauling in UAV-assisted wireless networks. IEEE Internet Things J 11(8):14727–14738

    Article  Google Scholar 

  53. Krijestorac E, Hanna S, Cabric D (2019) “UAV access point placement for connectivity to a user with unknown location using deep RL,” 2019 IEEE Globecom Workshops (GC Wkshps), pp 1-6

  54. Zhou X, Gao F, Fang X, Lan Z (2021) Improv bat algorithm for UAV path planning in three-dimensional space. IEEE Access 9:20100–20116

    Article  Google Scholar 

  55. Chen Y, Pi D, Xu Y (2021) Neighborhood global learning based flower pollination algorithm and its application to unmanned aerial vehicle path planning. Expert Syst Appl 170:114505

    Article  Google Scholar 

  56. Chen Y, Pi D, Wang B, Mohamed AW, Chen J, Wang Y (2024) Equilibrium optimizer with generalized opposition-based learning for multiple unmanned aerial vehicle path planning. Soft Comput 28(7):6185–6198

    Article  Google Scholar 

  57. Bandari S, Nirmala Devi L (2024) “A multi-objective approach for optimal target coverage UAV placement: hybrid heuristic formulation,” J Control and Decis, pp 1-17

  58. Zhao K, Razaq MM, Peng L, Tak B (2023) 3D placement of UAVs with optimal beamforming for multi-user communications. Computers Electr Eng 108:108665

    Article  Google Scholar 

  59. Sun Y, Wang T, Wang S, Member S (2019) Location optimization and user association for unmanned aerial vehicles assisted mobile networks. IEEE Trans Veh Technol 68(10):10056–10065

    Article  Google Scholar 

  60. Zahedi MH, Sobouti MJ, Mohajerzadeh AH, Rezaee AA, Seno SAH (2020) Fuzzy based efficient drone base stations (DBSs) placement in the 5G cellular network. Iran J Fuzzy Syst 17(2):29–38

    MathSciNet  Google Scholar 

  61. Liu Y, Huangfu W, Zhou H, Zhang H, Liu J (2022) Fair and energy-efficient coverage optimization for UAV placement problem in the cellular network. IEEE Trans Commun 70(6):4222–4235

    Article  Google Scholar 

  62. Salameh HB, Othman A, Alhafnawi M (2024) Optimized charging-station placement and UAV trajectory for enhanced uncertain target detection in intelligent UAV tracking systems. Int J Cognit Comput Eng 5:367–378

    Google Scholar 

  63. Abualigah L, Diabat A, Mirjalili S, Abd M (2021) The arithmetic optimization algorithm. Computer Methods Appl Mech Eng 376:113609

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

The present study was carried out with the financial support of the University of Bonab (project No. 140230).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Bonab, Bonab, Iran

    Hojjat Emami

Authors
  1. Hojjat Emami
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

This paper presents an efficient local search-based arithmetic algorithm for unmanned aerial vehicle placement in MANET. The proposed algorithm obtains better performance compared with its counterparts in most UAV placement test cases.

Corresponding author

Correspondence to Hojjat Emami.

Ethics declarations

Conflict of interest

H. Emami declares that he has no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Emami, H. An adaptive local search-based arithmetic optimization algorithm for unmanned aerial vehicle placement. J Supercomput 81, 302 (2025). https://doi.org/10.1007/s11227-024-06812-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06812-4

Keywords