Decision-Making of Underwater Cooperative Confrontation Based on MODPSO

Sensors - Tập 19 Số 9 - Trang 2211
Na Wei1,2, Mingyong Liu1, Weibin Cheng2
1School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Well, Xi'an Shiyou University, Xi'an 710065, China

Tóm tắt

This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.

Từ khóa


Tài liệu tham khảo

Babel, 2018, Coordinated target allocation and UAV path planning with timing constraints, J. Intell. Rob. Syst., 94, 1

Beard, R.W., Mclain, T.W., and Goodrich, M. (2002, January 11–15). Coordinated target allocation and intercept for unmanned air vehicles. Proceedings of the IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Washington, DC, USA.

Garcia, E., and Casbeer, D. (2013, January 19–22). UAV Cooperative Task Allocation with Communication Delays and Conflict Resolution. Proceedings of the AIAA Infotech@Aerospace (I@A) Conference, Boston, MA, USA.

Baharian, 2014, Limiting behavior of the target-dependent stochastic sequential allocation problem, J. Appl. Probab., 4, 943, 10.1239/jap/1421763320

Koenig, S., and Ma, H. (2016, January 9–13). Optimal target allocation and path finding for teams of agents. Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems, Singapore.

Chopra, 2017, A Distributed Version of the Hungarian Method for Multirobot Assignment, IEEE Trans. Rob., 33, 932, 10.1109/TRO.2017.2693377

Cheng, 2015, Weapon Target Assignment Problem Solving Based on Hungarian Algorithm, Appl. Mech. Mater., 713–715, 2041, 10.4028/www.scientific.net/AMM.713-715.2041

Juan, L.I., Jie, C., and Bin, X. (2015, January 28–30). Efficiently solving multi-objective dynamic weapon-target assignment problems by NSGA-II. Proceedings of the 34th Chinese Control Conference (CCC), Hangzhou, China.

Dong, 2016, Improved Genetic Algorithm for Solving Firepower Distribution, Acta Armamentarii, 37, 97

Lee, 2002, An immunity-based ant colony optimization algorithm for solving weapon–target assignment problem, Appl. Soft Comput., 2, 39, 10.1016/S1568-4946(02)00027-3

Ma, 2011, Hybrid strategy with ant colony and simulated annealing algorithm and its improvement in target assignment, Syst. Eng. Electron., 33, 1182

Xu, 2018, WTA for air and missile defense based on fuzzy multi-objective programming, Syst. Eng. Electron., 40, 563

Cruz, J.J., Chen, C., Li, D., and Wang, X. (2004, January 16–19). Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control. Proceedings of the AAIA Guidance, Navigation, and Control Conference and Exhibit, Providence, Rhode Island.

Liang, 2016, Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application, Optik, 127, 3459, 10.1016/j.ijleo.2015.12.122

Muhammed, D., Anisi, M.H., Zareei, M., Vargas-Rosales, C., and Khan, A. (2018). Game theory-based cooperation for underwater acoustic sensor networks: Taxonomy, review, research challenges and directions. Sensors, 18.

Leboucher, 2013, Optimal weapon target allocation based on an geometric approach, IFAC Proc. Volumes., 46, 341, 10.3182/20130902-5-DE-2040.00037

Tokgöz, A., and Bulkan, S. (2013). Weapon target allocation with combinatorial optimization techniques. Int. J. Adv. Res. Artif. Intell., 2.

Ahuja, 2007, Exact and heuristic algorithms for the weapon-target allocation problem, Oper. Res., 55, 1136, 10.1287/opre.1070.0440

Van Den Bergh, F. (2002). An analysis of particle swarm optimizers. [Ph.D. Thesis, University of Pretoria].

Parrott, 2006, Locating and tracking multiple dynamic optima by a particle swarm model using speciation, IEEE Trans. Evol. Comput., 10, 440, 10.1109/TEVC.2005.859468

Zhang, Y., and Gong, D.W. (2016). Advanced Multi-Objective Particle Swarm Optimization Theory and Application, Science Press.

Wei, N., and Liu, M.Y. (2017, January 5–8). AUV antagonistic tactics study for naval battle based on dynamic game. Proceedings of the IEEE International Conference on Robotics and Biomimetics(ROBIO), Macau, China.

Wei, 2019, Optimizing underwater game strategy based on cooperative confrontation, J. Northwestern Polytechnical Univ., 37, 63, 10.1051/jnwpu/20193710063

Fan, 2015, Weapon-target allocation optimization algorithm based on IDPSO, Syst. Eng. Electron., 37, 336

Li, 2010, Weapon-target assignment based on simulated annealing and discrete particle swarm optimization in cooperative air combat, Acta Aeronaut. Astronaut. Sin., 31, 626

Xin, 2010, Efficient Decision Makings for Dynamic Weapon-Target Assignment by Virtual Permutation and Tabu Search Heuristics, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., 40, 649, 10.1109/TSMCC.2010.2049261

Xin, 2011, An Efficient Rule-Based Constructive Heuristic to Solve Dynamic Weapon-Target Assignment Problem, IEEE Trans. Syst. Man Cybern. Part A Syst. Hum., 41, 598, 10.1109/TSMCA.2010.2089511

2014, Approximating the optimal mapping for weapon target assignment by fuzzy reasoning, Inf. Sci., 255, 30, 10.1016/j.ins.2013.08.004

Lotter, 2013, A multi-objective approach towards weapon assignment in a ground based air defence environment, ORION, 29, 31, 10.5784/29-1-138

Lotter, 2014, Weapon assignment decision support in a surface based air defense environment, Mil. Oper. Res., 19, 4052

Li, J., Chen, J., Xin, B., and Dou, L. (2015, January 25–28). Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGAII and adaptive MOEA/D: A comparison study. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan.

Li, J., Chen, J., Xin, B., and Chen, L. (2017, January 5–8). Efficient multi-objective evolutionary algorithms for solving the multi-stage weapon target assignment problem: A comparison study. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain.

Deb, 2002, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017

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Sensors

Tập 19 Số 9

2211

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