Skip to main content
SpringerLink
Log in

Hawk and pigeon’s intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

For unmanned aerial vehicle (UAV) swarm dynamic combat, swarm antagonistic motion control and attack target allocation are extremely challenging sub-tasks. In this paper, the competitive learning pigeon-inspired optimization (CLPIO) algorithm is proposed to handle the cooperative dynamic combat problem, which integrates the distributed swarm antagonistic motion and centralized attack target allocation. Moreover, the threshold trigger strategy is presented to switch two sub-tasks. To seek a feasible and optimal combat scheme, a dynamic game approach combined with hawk grouping mechanism and situation assessment between sub-groups is designed to guide the solution of the optimal attack scheme, and the model of swarm antagonistic motion imitating pigeon’s intelligence is proposed to form a confrontation situation. The analysis of the CLPIO algorithm shows its convergence in theory and the comparison with the other four metaheuristic algorithms shows its superiority in solving the mixed Nash equilibrium problem. Finally, numerical simulation verifis that the proposed methods can provide an effective combat scheme in the set scenario.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Xu J, Deng Z, Song Q, et al. Multi-UAV counter-game model based on uncertain information. Appl Math Comput, 2020, 366: 124684

    MathSciNet  Google Scholar 

  2. Chen J, Chen P, Wu Q, et al. A game-theoretic perspective on resource management for large-scale UAV communication networks. China Commun, 2021, 18: 70–87

    Article  Google Scholar 

  3. Fan J, Li D, Li R, et al. Analysis on MAV/UAV cooperative combat based on complex network. Defence Tech, 2020, 16: 150–157

    Article  Google Scholar 

  4. Luo D L, Xu Y, Zhang J P. New progresses on UAV swarm confrontation (in Chinese). Sci Tech Rev, 2017, 35: 26–31

    Google Scholar 

  5. Duan H B, Li P, Yu Y X. A predator-prey particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory. IEEE CAA J Autom Sin, 2015, 2: 11–18

    Article  MathSciNet  Google Scholar 

  6. Huo M, Duan H, Fan Y. Pigeon-inspired circular formation control for multi-UAV system with limited target information. Guid Navigat Control, 2021, 1: 2150004

    Article  Google Scholar 

  7. Vicsek T, Czirók A, Ben-Jacob E, et al. Novel type of phase transition in a system of self-driven particles. Phys Rev Lett, 1995, 75: 1226–1229

    Article  MathSciNet  Google Scholar 

  8. Couzin I D, Krause J, James R, et al. Collective memory and spatial sorting in animal groups. J Theor Biol, 2002, 218: 1–11

    Article  MathSciNet  Google Scholar 

  9. Reynolds C W. Flocks, herds, and schools: A distributed behavioral model. J Theor Biol, 1987, 21: 25–34

    Google Scholar 

  10. Vásárhelyi G, Virágh C, Somorjai G, et al. Optimized flocking of autonomous drones in confined environments. Sci Robot, 2018, 3: 1–13

    Article  Google Scholar 

  11. Jia Y, Vicsek T. Modelling hierarchical flocking. New J Phys, 2019, 21: 1–12

    Article  MathSciNet  Google Scholar 

  12. Choi H L, Brunet L, How J P. Consensus-based decentralized auctions for robust task allocation. IEEE Trans Robot, 2009, 25: 912–926

    Article  Google Scholar 

  13. Li M, Liu C, Li K, et al. Multi-task allocation with an optimized quantum particle swarm method. Appl Soft Computing, 2020, 96: 106603

    Article  Google Scholar 

  14. Lee D H, Zaheer S A, Kim J H. A resource-oriented, decentralized auction algorithm for multirobot task allocation. IEEE Trans Automat Sci Eng, 2015, 12: 1469–1481

    Article  Google Scholar 

  15. Chen Y, Yang D, Yu J. Multi-UAV task assignment with parameter and time-sensitive uncertainties using modified two-part wolf pack search algorithm. IEEE Trans Aerosp Electron Syst, 2018, 54: 2853–2872

    Article  Google Scholar 

  16. Luo D L, Zhang H Y, Xie R Z. Unmanned aerial vehicles swarm conflict based on multi-agent system (in Chinese). Control Theory Appl, 2015, 32: 1498–1504

    MATH  Google Scholar 

  17. Kang Y M, Liu Z, Pu Z Q. Beyond-visual-range tactical game strategy for multiple UAVs. In: Proceedings of the 2019 Chinese Automation Congress. Hangzhou, 2019. 5231–5236

  18. Chae H J, Choi H L. Tactics games for multiple UCAVs within-visual-range air combat. In: Proceedings of the 2018 AIAA Information Systems-AIAA Infotech @ Aerospace. Kissimmee, 2018. 1–10

  19. Moya S. The calculation of the Stackelberg-Nash equilibrium as a fixed point problem in static hierarchical games. Int J Dynam Control, 2018, 6: 907–918

    Article  MathSciNet  Google Scholar 

  20. Chow C K, Yuen S Y. An evolutionary algorithm that makes decision based on the entire previous search history. IEEE Trans Evol Computat, 2011, 15: 741–769

    Article  Google Scholar 

  21. Riwanto B A, Tikka T, Kestila A, et al. Particle swarm optimization with rotation axis fitting for magnetometer calibration. IEEE Trans Aerosp Electron Syst, 2017, 53: 1009–1022

    Article  Google Scholar 

  22. Liu C B, Ma Y H, Yin H, et al. Human resource allocation for multiple scientific research projects via improved pigeon-inspired optimization algorithm. Sci China Tech Sci, 2021, 64: 139–147

    Article  Google Scholar 

  23. Huang H, Dong K, Yan T, et al. Tactical maneuver trajectory optimization for unmanned combat aerial vehicle using improved differential evolution. Soft Comput, 2020, 24: 5959–5970

    Article  Google Scholar 

  24. Duan H, Qiao P. Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning. Int J Intelligent Comput Cybernet, 2014, 7: 24–37

    Article  MathSciNet  Google Scholar 

  25. Chen L, Duan H B, Fan Y M, et al. Multi-objective clustering analysis via combinatorial pigeon inspired optimization. Sci China Tech Sci, 2020, 63: 1302–1313

    Article  Google Scholar 

  26. Pei J Z, Su Y X, Zhang D H. Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm. Sci China Tech Sci, 2017, 60: 425–433

    Article  Google Scholar 

  27. Xin L, Xian N. Biological object recognition approach using space variant resolution and pigeon-inspired optimization for UAV. Sci China Tech Sci, 2017, 60: 1577–1584

    Article  Google Scholar 

  28. Qiu H, Duan H. A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Inf Sci, 2020, 509: 515–529

    Article  MathSciNet  Google Scholar 

  29. Zhang D, Duan H. Social-class pigeon-inspired optimization and time stamp segmentation for multi-UAV cooperative path planning. Neurocomputing, 2018, 313: 229–246

    Article  Google Scholar 

  30. Duan H, Huo M, Yang Z, et al. Predator-prey pigeon-inspired optimization for UAV ALS longitudinal parameters tuning. IEEE Trans Aerosp Electron Syst, 2019, 55: 2347–2358

    Article  Google Scholar 

  31. Pettit B, Andrea P, Dora B. Interaction rules underlying group decisions in homing pigeons. J R Soc Interface, 2013, 10: 1742–5662

    Article  Google Scholar 

  32. Zhang H T, Chen Z, Vicsek T, et al. Route-dependent switch between hierarchical and egalitarian strategies in pigeon flocks. Sci Rep, 2015, 4: 5805

    Article  Google Scholar 

  33. Xing D, Zhen Z, Gong H. Offense-defense confrontation decision making for dynamic UAV swarm versus UAV swarm. Proc Inst Mech Engineers Part G-J Aerospace Eng, 2019, 233: 5689–5702

    Article  Google Scholar 

  34. Duan H, Qiu H. Advancements in pigeon-inspired optimization and its variants. Sci China Inf Sci, 2019, 62: 070201

    Article  MathSciNet  Google Scholar 

  35. Cheng R, Jin Y C. A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern, 2015, 45: 191–204

    Article  Google Scholar 

  36. Li X. Niching without niching parameters: particle swarm optimization using a ring topology. IEEE Trans Cybern, 2010, 14: 150–169

    Google Scholar 

  37. Zhang Q, Cheng H, Ye Z C. A competitive swarm optimizer integrated with cauchy and gaussian mutation for large scale optimization. In: Proceedings of the 36th Chinese Control Conference. Dalian, 2017. 9829–9834

  38. Duan H, Huo M, Shi Y. Limit-cycle-based mutant multiobjective pigeon-inspired optimization. IEEE Trans Evol Computat, 2020, 24: 948–959

    Article  Google Scholar 

  39. Zhang Y, Huang H, Wu H, et al. Theoretical analysis of the convergence property of a basic pigeon-inspired optimizer in a continuous search space. Sci China Inf Sci, 2019, 62: 70207

    Article  MathSciNet  Google Scholar 

  40. Zhou J H, Fang W, Wu X J. An opposition-based learning competitive particle swarm optimizer. In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation. Vancouver, 2016. 515–521

Download references

Author information

Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China

    YuePing Yu & Chen Wei

  2. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    JiChuan Liu

  3. Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control, The 54th Research Institute of CETC, Shijiazhuang, 050081, China

    JiChuan Liu

Authors
  1. YuePing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JiChuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Wei.

Additional information

This work was partially supported by the Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence” (Grant No. 2018AAA0102403), and the National Natural Science Foundation of China (Grant Nos. U20B2071, 91948204, T2121003, and U1913602).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Liu, J. & Wei, C. Hawk and pigeon’s intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization. Sci. China Technol. Sci. 65, 1072–1086 (2022). https://doi.org/10.1007/s11431-021-1951-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-021-1951-9

Keywords

Search

Navigation