A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 1
Jan.  2015

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Haibin Duan, Pei Li and Yaxiang Yu, "A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 11-18, 2015.
Citation: Haibin Duan, Pei Li and Yaxiang Yu, "A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 11-18, 2015.

A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory


This work was supported by National Natural Science Foundation of China (61425008, 61333004, 61273054), Top-Notch Young Talents Program of China, and Aeronautical Foundation of China (2013585104).

  • Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue.


  • loading
  • [1]
    Richards A, Bellingham J, Tillerson M, How J. Coordination and control of multiple UAVs. In:Proceedings of the 2002 AIAA Guidance, Navigation, and Control Conference. Monterey, CA:AIAA, 2002. 145-146
    Alighanbari M, Kuwata Y, How J P. Coordination and control of multiple UAVs with timing constraints and loitering. In:Proceedings of the 2003 American Control Conference. Denver, Colorado:IEEE, 2003. 5311-5316
    Li C S, Wang Y Z. Protocol design for output consensus of portcontrolled Hamiltonian multi-agent systems. Acta Automatica Sinica, 2014, 40(3):415-422
    Duan H, Li P. Bio-inspired Computation in Unmanned Aerial Vehicles. Berlin:Springer-Verlag, 2014. 143-181
    Duan H, Shao S, Su B, Zhang L. New development thoughts on the bioinspired intelligence based control for unmanned combat aerial vehicle. Science China Technological Sciences, 2010, 53(8):2025-2031
    Chi P, Chen Z J, Zhou R. Autonomous decision-making of UAV based on extended situation assessment. In:Proceedings of the 2006 AIAA Guidance, Navigation, and Control Conference and Exhibit. Colorado, USA:AIAA, 2006.
    Ruz J J, Arelo O, Pajares G, de la Cruz J M. Decision making among alternative routes for uavs in dynamic environments. In:Proceedings of the 2007 IEEE Conference on Emerging Technologies and Factory Automation. Patras:IEEE, 2007. 997-1004
    Jung S, Ariyur K B. Enabling operational autonomy for unmanned aerial vehicles with scalability. Journal of Aerospace Information Systems, 2013, 10(11):516-529
    Berger J, Boukhtouta A, Benmoussa A, Kettani O. A new mixed-integer linear programming model for rescue path planning in uncertain adversarial environment. Computers & Operations Research, 2012, 39(12):3420-3430
    Duan H B, Liu S. Unmanned air/ground vehicles heterogeneous cooperative techniques:current status and prospects. Science China Technological Sciences, 2010, 53(5):1349-1355
    Cruz Jr J B, Simaan M A, Gacic A, Jiang H, Letelliier B, Li M, Liu Y. Game-theoretic modeling and control of a military air operation. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(4):1393-1405
    Dixon W. Optimal adaptive control and differential games by reinforcement learning principles. Journal of Guidance, Control, and Dynamics, 2014, 37(3):1048-1049
    Semsar-Kazerooni E, Khorasani K. Multi-agent team cooperation:a game theory approach. Automatica, 2009, 45(10):2205-2213
    Gu D. A game theory approach to target tracking in sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2011, 41(1):2-13
    Duan H, Wei X, Dong Z. Multiple UCAVs cooperative air combat simulation platform based on PSO, ACO, and game theory. IEEE Aerospace and Electronic Systems Magazine, 2013, 28(11):12-19
    Turetsky V, Shinar J. Missile guidance laws based on pursuit-evasion game formulations. Automatica, 2003, 39(4):607-618
    Porter R, Nudelman E, Shoham Y. Simple search methods for finding a Nash equilibrium. Games and Economic Behavior, 2008, 63(2):642-662
    Chen X, Deng X, Teng S-H. Settling the complexity of computing twoplayer Nash equilibria. Journal of the ACM, 2009, 56(3):Article No. 14
    Kennedy J, Eberhart R. Particle swarm optimization. In:Proceedings of the 1st IEEE International Conference on Neural Networks. Perth, Australia:IEEE, 1995. 1942-1948
    Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In:Proceedings of the 6th International Symposium on Micro Machine and Human Science. Nagoya:IEEE, 1995. 39-43
    Higashitani M, Ishigame A, Yasuda K. Particle swarm optimization considering the concept of predator-prey behavior. In:Proceedings of the 2006 IEEE Congress on Evolutionary Computation. Vancouver, BC, Canada:IEEE, 2006. 434-437
    Liu F, Duan H B, Deng Y M. A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching. Optik-International Journal for Light and Electron Optics, 2012, 123(21):1955-1960
    Edison E, Shima T. Genetic algorithm for cooperative UAV task assignment and path optimization. In:Proceedings of the 2008 AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, Hawaii:AIAA, 2008. 340-356
    Duan H, Luo Q, Shi Y, Ma G. Hybrid particle swarm optimization and genetic algorithm for multi-UAV formation reconfiguration. IEEE Computational Intelligence Magazine, 2013, 8(3):16-27
    Liu G, Lao S Y, Tan D F, Zhou Z C. Research status and progress on anti-ship missile path planning. Acta Automatica Sinica, 2013, 39(4):347-359
    Duan H B, Yu Y X, Zhao Z Y. Parameters identification of UCAV flight control system based on predator-prey particle swarm optimization. Science China Information Sciences, 2013, 56(1):1-12
    Duan H, Li S, Shi Y. Predator-prey based brain storm optimization for DC brushless motor. IEEE Transactions on Magnetics, 2013, 49(10):5336-5340
    Pan F, Li X T, Zhou Q, Li W X, Gao Q. Analysis of standard particle swarm optimization algorithm based on Markov chain. Acta Automatica Sinica, 2013, 39(4):381-389
    Nash J F. Equilibrium points in n-person games. Proceedings of the National Academy of Sciences of the United States of America, 1950, 36(1):48-49
    Yu Qian, Wang Xian-Jia. Evolutionary algorithm for solving Nash equilibrium based on particle swarm optimization. Journal of Wuhan University (Natural Science Edition), 2006, 52(1):25-29(in Chinese)


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1136) PDF downloads(14) Cited by()


    DownLoad:  Full-Size Img  PowerPoint