Skip to main content
Have a personal or library account? Click to login
Research on UAV Dynamic Adversarial Game Strategies using an Improved Marine Predator Algorithm Cover

Research on UAV Dynamic Adversarial Game Strategies using an Improved Marine Predator Algorithm

Open Access
|Jun 2026

References

  1. Duan, H., Li, P. and Yu, Y. (2015). A predator-prey particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory, IEEE/CAA Journal of Automatica Sinica 2(1): 11–18.
  2. Fan, Q., Huang, H., Chen, Q., Yao, L., Yang, K. and Huang, D. (2022). A modified self-adaptive marine predators algorithm: Framework and engineering applications, Engineering with Computers 38(4): 3269–3294.
  3. Faramarzi, A., Heidarinejad, M., Mirjalili, S., and Gandomi, A.H. (2020). Marine predators algorithm: A nature-inspired metaheuristic, Expert Systems with Applications 152: 113377, DOI: 10.1016/j.eswa.2020.113377.
  4. Fu, X., Liu, S.L., Guan, Z.F. and Liu, H. (2023). Phased-improvement marine predators algorithm and its application, Control and Decision 38(4): 902–910.
  5. Fu, X., Zhu, J., Wei, Z., Wang, H. and Li, S. (2022). A UAV pursuit-evasion strategy based on DDPG and imitation learning, International Journal of Aerospace Engineering 2022(1): 3139610.
  6. Gong, C., Zhou, N., Xia, S. and Huang, S. (2024). Quantum particle swarm optimization algorithm based on diversity migration strategy, Future Generation Computer Systems 157: 445–458.
  7. Jiang, G.-s., Shi, X.-m., Chen, J., Zhao, M. and Liu, H.-b. (2022). Dynamic weapon target assignment based on incomplete information game, Command Control and Simulation 44(3): 20–24.
  8. Kantue, P. and Pedro, J.O. (2022). Integrated fault-tolerant control of a quadcopter UAV with incipient actuator faults, International Journal of Applied Mathematics and Computer Science 32(4): 607–617, DOI: 10.34768/amcs-2022-0042.
  9. Kim, J., Oh, H., Yu, B. and Kim, S. (2021). Optimal task assignment for UAV swarm operations in hostile environments, International Journal of Aeronautical and Space Sciences 22(2): 456–467.
  10. Li, S., Ding, Y. and Gao, Z. (2019). UAV air combat maneuvering decision based on intuitionistic fuzzy game theory, Systems Engineering and Electronics 41(5): 1063–1070.
  11. Li, Y., Li, J., Liu, C., Li, J., Xin, Z. and Chen, Z. (2022). An auction-based attack-defense decision-making method for UAV air combat, 2022 IEEE International Conference on Unmanned Systems (ICUS), Guangzhou, China, pp. 902–909.
  12. Liu, C., Sun, S., Tao, C., Shou, Y. and Xu, B. (2021a). Sliding mode control of multi-agent system with application to UAV air combat, Computers & Electrical Engineering 96(A): 107491, DOI: 10.1016/j.compeleceng.2021.107491.
  13. Liu, L., Zhang, S., Zhang, L., Pan, G. and Bai, C. (2021b). Multi-AUV dynamic maneuver decision-making based on intuitionistic fuzzy counter-game and fractional-order particle swarm optimization, Fractals 29(08): 2140039.
  14. Liu, F., Dong, X., Yu, J., Hua, Y., Li, Q. and Ren, Z. (2022a). Distributed Nash equilibrium seeking of n-coalition noncooperative games with application to UAV swarms, IEEE Transactions on Network Science and Engineering 9(4): 2392–2405.
  15. Liu, H., Wang, Y., Chen, M. and Zhang, Y. (2022b). UAV air combat game based on iteration method, Electronics Optics & Control 29(2): 1–6.
  16. Liu, J., Wu, Q., Wang, Y. and Zhou, D. (2022c). UAV game decision based on quantum particle swarm optimization, Fire Control and Command Control 47(09): 73–78+84.
  17. Ma, C., Zeng, G.H., Huang, B. and Liu, J. (2022). Marine predator algorithm based on chaotic opposition learning and group learning, Computer Engineering and Applications 58: 271–283.
  18. Meng, Y., He, J., Luo, S., Tao, S. and Xu, J. (2021). An improved predator-prey particle swarm optimization algorithm for nash equilibrium solution, PLOS One 16(11): e0260231.
  19. Merheb, A.-R., Noura, H. and Bateman, F. (2015). Design of passive fault-tolerant controllers of a quadrotor based on sliding mode theory, International Journal of Applied Mathematics and Computer Science 25(3): 561–576, DOI: 10.1515/amcs-2015-0042.
  20. Oszust, M. (2021). Enhanced marine predators algorithm with local escaping operator for global optimization, Knowledge-Based Systems 232: 107467, DOI: 10.1016/j.knosys.2021.107467.
  21. Özdemir, R., Taşyürek, M. and Aslantaş, V. (2024). Improved marine predators algorithm and extreme gradient boosting (XGBoost) for shipment status time prediction, Knowledge-Based Systems 294: 111775, DOI: 10.1016/j.knosys.2024.111775.
  22. Ramezani, M., Bahmanyar, D. and Razmjooy, N. (2021). A new improved model of marine predator algorithm for optimization problems, Arabian Journal for Science and Engineering 46(9): 8803–8826.
  23. Salazar, J.C., Sanjuan Gómez, A., Nejjari Akhi-Elarab, F. and Sarrate Estruch, R. (2020). Health-aware and fault-tolerant control of an octorotor UAV system based on actuator reliability, International Journal of Applied Mathematics and Computer Science 30(1): 47–59, DOI: 10.34768/amcs-2020-0004.
  24. Song, Y., Cai, X., Zhou, X. Zhang, Bin, C., Huiling, L., Yuangang, D., Wuquan, D., Wu (2023). Dynamic hybrid mechanism-based differential evolution algorithm and its application, Expert Systems with Applications 213: 118834, Part: A, DOI: 10.1016/j.eswa.2022.118834.
  25. Tizhoosh, H.R. (2005). Opposition-based learning: A new scheme for machine intelligence, International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA–IAWTIC’06), Austria, Vol. 1, Vienna, pp. 695–701.
  26. Wang, Y., Zhang, W., Fu, L., Huang, D. and Li, Y. (2015). Nash equilibrium strategies approach for aerial combat based on elite reelection particle swarm optimization, Control Theory and Applications 32(7): 857–865.
  27. Xu, J., Deng, Z., Song, Q., Chi, Q., Wu, T., Huang, Y., Liu, D. and Gao, M. (2020). Multi-UAV counter-game model based on uncertain information, Applied Mathematics and Computation 366: 124684, DOI: 10.1016/j.amc.2019.124684.
  28. Yu, Y., Liu, J. and Wei, C. (2022). Hawk and pigeon’s intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization, Science China Technological Sciences 65(5): 1072–1086.
  29. Zhang, Q., Zeng, Q., Zhang, Z. and Ye, U.X.Y. (2022). Research on weapon-target assignment problem for marine predator algorithm, Journal of Ordnance Equipment Engineering 43(8): 158–163.
  30. Zhao, Y.L., Song, Y.X., Zhang, J.J. and Kang, L.W. (2019). Fuzzy game decision-making of unmanned aerial vehicles air-to-ground attack based on the particle swarm optimization integrating multiply strategies, Control Theory and Applications 36(10): 1644–1652.
DOI: https://doi.org/10.61822/amcs-2026-0014 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 195 - 209
Submitted on: Oct 13, 2025
Accepted on: Mar 25, 2026
Published on: Jun 20, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2026 Nannan Cheng, Qing Li, Heng Wang, Saisai Tong, Xingjian Fu, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.