Cluster Computing

, Volume 19, Issue 3, pp 1359–1372 | Cite as

Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment

  • Shanliang Yang
  • Mei Yang
  • Song Wang
  • Kedi Huang


Military applications are producing massive amounts of data due to the use of multiple types of sensors on the battlefield. The aim of this paper is to investigate the weapon system portfolio problem with the valuable knowledge extracted from these sensor data. The objective of weapon system portfolio optimization is to determine the appropriate assignment of various weapon units, which maximizes the expected damage of all hostile targets, while satisfying a set of constraints. This paper presents a mixed integer non-linear optimization model for the weapon system portfolio problem. In order to solve this model, an adaptive immune genetic algorithm using crossover and mutation probabilities that are automatically tuned in each generation is proposed. A ground-based air defensive scenario is introduced to illustrate the feasibility and efficiency of our proposed algorithm. In addition, several large-scale instances that are produced by a test-case generator are also considered to demonstrate the scalability of the algorithm. Comparative experiments have shown that our algorithm outperforms its competitors in terms of convergence speed and solution quality, and it is competent for solving weapon system portfolio problems under different scales.


Weapon system portfolio problem Integer non-linear optimization model Adaptive immune genetic algorithm (AIGA) Test-case generator 



The authors are grateful to the reviewers and the editor for their constructive comments and suggestions which are very helpful in improving the quality of the paper. This research is financially supported by National Natural Science Foundation of China under Grant Nos. 61074108 & 61374185.

Compliance with ethical standards

Conflicts of interest

The author declares that there is no conflict of interests regarding the publication of this paper.


  1. 1.
    James, M., Michael, C., Brad, B., et al.: Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute (2011)Google Scholar
  2. 2.
    Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59 (2013)CrossRefGoogle Scholar
  3. 3.
    McAfee, A., Brynjolfsson, E.: Big data: the management revolution. Harv. Bus. Rev. 90(10), 61–67 (2012)Google Scholar
  4. 4.
    Song, X., Wu, Y., Ma, Y., Cui, Y., Gong, G.: Military simulation big data: background, state of the art, and challenges. Math. Probl. Eng., pp., 1–20 (2015). Article ID: 298356. doi: 10.1155/2015/298356
  5. 5.
    Wu, W., Guo, S., He, X., Hu, X.: Research on temporal network of combat SoS coordination based on big data. J. Command Control. 1(2), 150–159 (2015)Google Scholar
  6. 6.
    McGregor, C., Bonnis, B., Stanfield, B., Stanfield, M.: A method for real-time stimulation and response monitoring using big data and its application to tactical training. In: IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 169–170 (2015)Google Scholar
  7. 7.
    Akhgar, B., Saathoff, G.B., Arabnia, H.R., Hill, R., et al.: Application of Big Data for National Security. Elsevier Butterworth-Heinemann, Oxford (2015)Google Scholar
  8. 8.
    Kulshrestha, S.: Big data in military information & intelligence. IndraStra Global. doi: 10.6084/m9.figshare.2066640, 2(1), 1–9 (2016)
  9. 9.
    Cha, Y.-H., Bang, J.-Y.: A branch-and-bound algorithm to minimize the makespan in a fire scheduling problem. J. Soc. Korea Ind. Syst. Eng. 38(4), 132–141 (2015)CrossRefGoogle Scholar
  10. 10.
    Sahin, M.A., Leblebicioglu, K.: Approximating the optimal mapping for weapon target assignment by fuzzy reasoning. Inf. Sci. 255, 30–44 (2014)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Elattar, E.E.: A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem. Electr. Power Energy Syst. 69, 18–26 (2015)CrossRefGoogle Scholar
  12. 12.
    Han, H., Ding, Y.S., Hao, K.R., Liang, X.: An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking. Comput. Math. Appl. 62, 2685–2695 (2011)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Ministry of National Defence of The People’s Republic of China.
  14. 14.
    Lee, J., Kang, S.-H., Rosenberger, J., Kim, S.B.: A hybrid approach of goal programming for weapon systems selection. Comput. Ind. Eng. 58, 521–527 (2010)CrossRefGoogle Scholar
  15. 15.
    Vie, L.L., Scheier, L.M., Lester, P.B., Ho, T.E.: The U.S. army person-event data environment: a military-civilian big data enterprise. Big Data 3, 1–13 (2015)CrossRefGoogle Scholar
  16. 16.
    Lee, Z.-J., Su, S.-F., Lee, C.-Y.: A genetic algorithm with domain knowledge for weapon-target assignment problems. J. Chin. Inst. Eng. 25(3), 287–295 (2002)CrossRefGoogle Scholar
  17. 17.
    Lee, M.Z.: Constrained weapon-target assignment: enhanced very large scale neighborhood search algorithm. IEEE Trans. Syst. Man Cybern. Part A 40(1), 198–204 (2010)CrossRefGoogle Scholar
  18. 18.
    Bogdanowicz, Z.R.: A new efficient algorithm for optimal assignment of smart weapons to targets. Comput. Math. Appl. 58, 1965–1969 (2009)CrossRefMATHGoogle Scholar
  19. 19.
    Bogdanowicz, Z.R., Tolano, A., Patel, K., Coleman, N.P.: Optimization of weapon-target pairings based on kill probabilities. IEEE Trans. Cybern. 43(6), 1835–1844 (2013)CrossRefGoogle Scholar
  20. 20.
    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. Part B 33(1), 113–120 (2003)CrossRefGoogle Scholar
  21. 21.
    Silven, S.: A neural approach to the assignment algorithm for multiple-target tracking. IEEE J. Ocean. Eng. 17(4), 326–332 (1992)CrossRefGoogle Scholar
  22. 22.
    Chen, J., Xin, B., Peng, Z., Dou, L., Zhang, J.: Evolutionary decision-makings for the dynamic weapon-target assignment problem. Sci. China Ser. F 52(11), 2006–2018 (2009)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Yanxia, W., Longjun, Q., Zhi, G., Lifeng, M.: Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm. J. Syst. Eng. Electron. 19(5), 939–944 (2008)CrossRefMATHGoogle Scholar
  24. 24.
    Wang, S., Chen, W., Gu, X.: Solving weapon-target assignment problems based on self-adaptive differential evolution algorithm. Syst. Eng. Electron. 35(10), 2115–2120 (2013)Google Scholar
  25. 25.
    Feng, G., Yan, M., Tong, F.: A hybrid quantum-based step tuning algorithm for weapon target assignment problem. Tactical Missile Technol. 6, 58–61 (2013)Google Scholar
  26. 26.
    Fan, C., Xing, Q., Zheng, M., Wang, Z.: Weapon-target allocation optimization algorithm based on IDPSO. Syst. Eng. Electron. 37(2), 336–342 (2015)Google Scholar
  27. 27.
    Yan, J., Li, X., Liu, L., Zhang, F.: Weapon-target assignment based on Memetic optimization algorithm in beyond-visual-rang cooperative air combat. J. Beijing Univ. Aeronaut. Astronaut. 40(10), 1424–1429 (2014)Google Scholar
  28. 28.
    Xuan, J., Luo, X., Zhang, G., Lu, J., Xu, Z.: Uncertainty analysis for the keyword system of web events. IEEE Trans. Syst. Man Cybern. 46(6), 829–842 (2016)CrossRefGoogle Scholar
  29. 29.
    Xu, Z., et al.: Semantic based representing and organizing surveillance big data using video structural description technology. J. Syst. Softw. 102, 217–225 (2015)CrossRefGoogle Scholar
  30. 30.
    Xu, Z., Mei, L., Hu, C., Liu, Y.: The big data analytics and applications of the surveillance system using video structured description technology. Clust. Comput. (2016). doi: 10.1007/s10586-016-0581-x
  31. 31.
    Day, R.H.: Allocating weapons to target complexes by means of non-linear programming. Operat. Res. 14, 992–1013 (1966)CrossRefGoogle Scholar
  32. 32.
    Gu, J.J., Zhao, J., Yan, J., Chen, X.: Cooperative weapon-target assignment based on multi-objective discrete particle swarm optimization-gravitational search algorithm in air combat. J. Beijing Univ. Aeronaut. Astronaut. 41(2), 252–258 (2015)Google Scholar
  33. 33.
    Xin, B., Chen, J., Peng, Z., Dou, L., Zhang, J.: An efficient rule-based constructive heuristic to solve dynamic weapon-target assignment problem. IEEE Trans. Syst. Man Cybern. Part A 41(3), 598–606 (2011)CrossRefGoogle Scholar
  34. 34.
    Ni, M.F., Yu, Z.K., Ma, F., Wu, X.R.: A lagrange relaxation method for solving weapon-target assignment problem. Math. Probl. Eng., pp. 1–10 (2011). Article ID: 873292, doi: 10.1155/2011/873292
  35. 35.
    Xin, B., Chen, J., Zhang, J., Dou, L., Peng, Zhihong: Efficient decision makings for dynamic weapon-target assignment by virtual permutation and Tabu search heuristics. IEEE Trans. Syst. Man Cybern. Part C 40(6), 649–662 (2010)CrossRefGoogle Scholar
  36. 36.
    Liao, G.C.: Integrated isolation niche and immune genetic algorithm for solving bid-based dynamic economic dispatch. Electr. Power Energy Syst. 42, 264–275 (2012)Google Scholar
  37. 37.
    Diabat, A., Kannan, D., Kaliyan, M., Svetinovic, D.: An optimization model for product returns using genetic algorithms and artificial immune system. Resour. Conserv. Recycl. 74, 156–169 (2013)CrossRefGoogle Scholar
  38. 38.
    Wang, D., Fung, R.Y.K., Ip, W.H.: An immune-genetic algorithm for introduction planning of new products. Comput. Ind. Eng. 56, 902–917 (2009)CrossRefGoogle Scholar
  39. 39.
    Chen, T.C., Hsieh, Y.C.: Using immune-based genetic algorithms for single trader’s periodic marketing problem. Math. Comput. Model. 48, 420–428 (2008)CrossRefMATHGoogle Scholar
  40. 40.
    Rabiej, M.: Application of immune and genetic algorithm to the identification of a polymer based on its X-ray diffraction curve. J. Appl. Crystallogr. 46, 1136–1144 (2013)CrossRefGoogle Scholar
  41. 41.
    Jiang, D.H., Hua, G.: Research on image enhancement method based on adaptive immune genetic algorithm. J. Comput. Theor. Nanosci. 12, 119–127 (2015)CrossRefGoogle Scholar
  42. 42.
    Arivudainambi, D., Rekha, D.: Broadcast scheduling problem in TDMA Ad Hoc Networks using immune genetic algorithm. Int. J. Comput. Commun. 8(1), 18–29 (2013)CrossRefGoogle Scholar
  43. 43.
    Zhang, L., Du, J., Shushan, Z.: Solution to the time-cost-quality trade-off problem in construction projects based on immune genetic particle swarm optimization. J. Manag. Eng. 30, 163–172 (2014)CrossRefGoogle Scholar
  44. 44.
    Liang, C., Peng, L.: An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J. Med. Syst. 37, 9932–9941 (2013)CrossRefGoogle Scholar
  45. 45.
    Duma, M., Marwala, T., Twala, B., Nelwamondo, F.: Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm. Appl. Soft Comput. 13, 4461–4480 (2013)CrossRefGoogle Scholar
  46. 46.
    Mahdavi, I., Movahednejad, M., Adbesh, F.: Designing customer-oriented catalogs in E-CRM using an effective self-adaptive genetic algorithm. Expert Syst. Appl. 38, 631–639 (2011)CrossRefGoogle Scholar
  47. 47.
    Xue, C., Dong, L., Liu, J.: Enterprise information system structure optimization based on time property with improved immune genetic algorithm and binary tree. Comput. Math. Appl. 63, 1155–1168 (2012)MathSciNetCrossRefMATHGoogle Scholar
  48. 48.
    Kuo, R.J., Lee, Y.H., Zulvia, F.E., Tien, F.C.: Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm. Appl. Math. Comput. 266, 1013–1026 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Shanliang Yang
    • 1
  • Mei Yang
    • 1
  • Song Wang
    • 1
  • Kedi Huang
    • 1
  1. 1.College of Information System and ManagementNational University of Defense TechnologyChangshaChina

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