Evolutionary decision-makings for the dynamic weapon-target assignment problem

  • Jie Chen
  • Bin Xin
  • ZhiHong Peng
  • LiHua Dou
  • Juan Zhang
Article

Abstract

The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general “virtual” representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.

Keywords

decision-making dynamic weapon-target assignment (DWTA) military command and control evolutionary computation memetic algorithms constraints handling 

References

  1. 1.
    Athans M. Command and control (C2) theory: a challenge to control science. IEEE Trans Automat Contr, 1987, 32(4): 286–293CrossRefGoogle Scholar
  2. 2.
    Lloyd S P, Witsenhausen H S. Weapon allocation is NP-complete. In: Proceedings of the IEEE Summer Computer Simulation Conference, Nevada, USA, 1986. 1054–1058Google Scholar
  3. 3.
    Hosein P A, Athans M. Preferential defense strategies. Part I: The static case. Report LIPS-P-2002. 1990Google Scholar
  4. 4.
    Hosein P A, Athans M. Preferential defense strategies. Part II: The dynamic case. Report LIPS-P-2003. 1990Google Scholar
  5. 5.
    Li J, Cong R, Xiong J. Dynamic WTA optimization model of air defense operation of warships’ formation. J Syst Eng Electr, 2006, 7(1): 126–131CrossRefGoogle Scholar
  6. 6.
    Cai H, Liu J, Chen Y, et al. Survey of the research on dynamic weapon-target assignment problem. J Syst Eng Electr, 2006, 17(3): 559–565CrossRefGoogle Scholar
  7. 7.
    Malhotra A, Jain R K. Genetic algorithm for optimal weapon allocation in multilayer defence scenario. Defence Sci J, 2001, 51(3): 285–293Google Scholar
  8. 8.
    Bisht S. Hybrid genetic-simulated annealing algorithm for optimal weapon allocation in multilayer defence scenario. Defence Sci J, 2004, 54(3): 395–405Google Scholar
  9. 9.
    Karasakal O. Air defense missile-target allocation models for a naval task group. Comput Oper Res, 2008, 35: 1759–1770MATHCrossRefGoogle Scholar
  10. 10.
    Wacholder E. A neural network-based optimization algorithm for the static weapon-target assignment problem. ORSA J Comput, 1989, 1(4): 232–246MATHGoogle Scholar
  11. 11.
    Grant K E. Optimal resource allocation using genetic algorithms. Naval Review, Naval Research Laboratory, Washington, DC, 1993. 174–175Google Scholar
  12. 12.
    Lu H, Zhang H, Zhang X, et al. An improved genetic algorithm for target assignment optimization of naval fleet air defense. In: Proceedings of the 6th World Congress on Intelligent Control & Automation, Dalian, China, 2006. 3401–3405Google Scholar
  13. 13.
    Cullenbine A C. A taboo search approach to the weapon assignment model. Master Thesis, Department of Operational Sciences, Air Force Institute of Technology, 2000Google Scholar
  14. 14.
    Li H R, Miao Y. WTA with the maximum kill probability based on simulated annealing algorithms. In: Proceedings of the Conference on Special Committee of C2 and Computer of the Electronic Technology Academic Committee of China, Ship Engineering Society, 2000. 436–440Google Scholar
  15. 15.
    Lee Z J, Lee C Y, Su S F. An immunity-based ant colony optimization algorithm for solving weapon-target assignment problem. Appl Soft Comput, 2002, 2: 39–47CrossRefGoogle Scholar
  16. 16.
    Zeng X, Zhu Y, Nan L, et al. Solving weapon-target assignment problem using discrete particle swarm optimization. In: Proceedings of the 6th World Congress on Intelligent Control & Automation, Dalian, China, 2006. 3562–3565Google Scholar
  17. 17.
    Lee Z J, Su S F, Lee C Y. Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics. IEEE Trans Syst Man Cybern-B, 2003, 33(1): 113–121CrossRefGoogle Scholar
  18. 18.
    Fu T, Liu Y, Chen J. Improved genetic & ant colony optimization for regional air defense WTA problem. In: Proceedings of the 1st International Conference on Innovative Computing, Information and Control (ICICIC’06), Dalian, China, 2006. 226–229Google Scholar
  19. 19.
    Ahuja R K, Kumar A, Jha K C, et al. Exact and heuristic algorithms for the weapon-target assignment problem. Oper Res, 2007, 55(6): 1136–1146MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Kwon O, Lee K, Kang D, et al. A branch-and-price algorithm for a targeting problem. Naval Res Logist, 2007, 54: 732–741MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Hosein P A, Walton J T, Athans M. Dynamic weapon-target assignment problems with vulnerable C2 nodes. Report LIDSP-1786. 1988Google Scholar
  22. 22.
    Hosein P A, Athans M. Some analytical results for the dynamic weapon-target allocation problem. Report LIDS-P-1944. 1990Google Scholar
  23. 23.
    Khosla D. Hybrid genetic approach for the dynamic weapon-target allocation problem. In: Proceedings of SPIE, 2001, 4396: 244–259Google Scholar
  24. 24.
    Havens M E. Dynamic allocation of fires and sensors. Master Thesis, Naval Postgraduate School, OMB No. 0704-0188, 2002Google Scholar
  25. 25.
    Loiola E M, Maia de Abreu N M, Boaventura Netto P O, et al. A survey for the quadratic assignment problem. Euro J Oper Res, 2007, 176: 657–690MATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Merz P, Freisleben B. Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput, 2000, 4(4): 337–352CrossRefGoogle Scholar
  27. 27.
    Krasnogor N, Smith J. A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput, 2005, 9(5): 474–488CrossRefGoogle Scholar
  28. 28.
    Chen J, Xin B, Peng Z H, et al. Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization. IEEE Trans Syst Man Cybern-A, 2009, 39(3): 680–691CrossRefGoogle Scholar

Copyright information

© Science in China Press and Springer Berlin Heidelberg 2009

Authors and Affiliations

  • Jie Chen
    • 1
    • 2
  • Bin Xin
    • 1
    • 2
  • ZhiHong Peng
    • 1
    • 2
  • LiHua Dou
    • 1
    • 2
  • Juan Zhang
    • 1
    • 2
  1. 1.School of AutomationBeijing Institute of TechnologyBeijingChina
  2. 2.Key Laboratory of Complex System Intelligent Control and DecisionMinistry of EducationBeijingChina

Personalised recommendations