Skip to main content
SpringerLink
Log in

Weapon-target assignment in unreliable peer-to-peer architecture based on adapted artificial bee colony algorithm

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

It is of great significance for headquarters in warfare to address the weapon-target assignment (WTA) problem with distributed computing nodes to attack targets simultaneously from different weapon units. However, the computing nodes on the battlefield are vulnerable to be attacked and the communication environment is usually unreliable. To solve the WTA problems in unreliable environments, this paper proposes a scheme based on decentralized peer-to-peer architecture and adapted artificial bee colony (ABC) optimization algorithm. In the decentralized architecture, the peer computing node is distributed to each weapon units and the packet loss rate is used to simulate the unreliable communication environment. The decisions made in each peer node will be merged into the decision set to carry out the optimal decision in the decentralized system by adapted ABC algorithm. The experimental results demonstrate that the decentralized peer-to-peer architecture perform an extraordinary role in the unreliable communication environment. The proposed scheme preforms outstanding results of enemy residual value (ERV) with the packet loss rate in the range from 0 to 0.9.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Guo D, Liang Z, Jiang P. Weapon-target assignment for multi-to-multi interception with grouping constraint. IEEE Access, 2019, 7: 34838–34849

    Article  Google Scholar 

  2. Cao M, Fang W. Swarm intelligence algorithms for weapon-target assignment in a multilayer defense scenario: a comparative study. Symmetry, 2020, 12(5): 824

    Article  Google Scholar 

  3. Sahin A M, Kemal L. Rule-based weapon target assignment on the battlefield. IFAC Proceedings Volumes, 2011, 44(1): 13600–13605

    Article  Google Scholar 

  4. Ni M, Yu Z, Ma F, Wu X. A lagrange relaxation method for solving weapon-target assignment problem. Mathematical Problems in Engineering, 2011, 2011: 1–10

    Article  MathSciNet  MATH  Google Scholar 

  5. Ruan C, Zhou Z, Liu H, Yang H. Task assignment under constraint of timing sequential for cooperative air combat. Journal of Systems Engineering and Electronics, 2016, 27(4): 836–844

    Article  Google Scholar 

  6. Kline A, Ahner D, Hill R. The weapon-target assignment problem. Computers & Operations Research, 2019, 105: 226–236

    Article  MathSciNet  MATH  Google Scholar 

  7. Shojaeifard A. Projection recurrent neural network model: a new strategy to solve weapon-target assignment problem. Neural Processing Letters, 2019, 50(3): 3045–3057

    Article  MathSciNet  Google Scholar 

  8. Gao C Q, Kou Y X. Multi-objective weapon target assignment based on D-NSGA-III-A. IEEE Access, 2019, 7: 50240–50254

    Article  Google Scholar 

  9. Goldberg D E. Genetic algorithm in search optimization and machine learning. In: Proceedings of Genetic Algorithms in Search Optimization and Machine Learning. 1989, 2104–2116

  10. Shi Y. Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation. 2001, 81–86

  11. Karaboga D, Akay B. A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 2009, 214(1): 108–132

    Article  MathSciNet  MATH  Google Scholar 

  12. Lee Z J, Su S F, Lee C Y. A genetic algorithm with domain knowledge for weapon-target assignment problems. Journal of the Chinese Institute of Engineers, 2002, 25(3): 287–295

    Article  Google Scholar 

  13. Lee Z J, Su S F, Lee C Y. Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics. IEEE Transactions on Systems Man, and Cybernetics Part B (Cybernetics), 2003, 33(1): 113–121

    Article  Google Scholar 

  14. Song Z H, Zhu F S, Zhang D L. A heuristic genetic algorithm for solving constrained Weapon-Target Assignment problem. In: Proceedings of IEEE International Conference on Intelligent Computing and Intelligent Systems. 2009, 336–341

  15. Zeng X P, Zhu Y L, Nan L. Solving weapon-target assignment problem using discrete particle swarm optimization. In: Proceedings of World Congress on Intelligent Control & Automation. 2006, 3562–3565

  16. Yang L, Zhai Z Z, Li Y H, Huang Y T. A multi-information particle swarm optimization algorithm for weapon target assignment of multiple kill vehicle. In: Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. 2018, 1160–1165

  17. Lee Z J, Lee C Y, Su S F. An immunity-based ant colony optimization algorithm for solving weapon-target assignment problem. Applied Soft Computing, 2002, 2(1): 39–47

    Article  Google Scholar 

  18. Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 2007, 39(3): 459–471

    Article  MathSciNet  MATH  Google Scholar 

  19. Cui L. A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Information Science, 2017, 414: 53–67

    Article  MathSciNet  MATH  Google Scholar 

  20. Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters. Communications of the ACM, 2008, 51(1): 107–113

    Article  Google Scholar 

  21. Coulouris G, Dollimore J, Kindberg T, Blair G. Distributed System: Concepts and Design. Pearson Education, 2005

  22. Lloyd S P, Witsenhausen H S. Weapons allocation is NP-complete. In: Proceedings of Summer Computer Simulation Conference. 1986, 1054–1058

  23. Bianchi L, Dorigo M, Gambardella L M, Gutjahr W J. A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 2009, 8(2): 239–287

    Article  MathSciNet  MATH  Google Scholar 

  24. Erik M, Pedersen H, Pedersen M. Good parameters for particle swarm optimization. Hvass Laboratories Technical Report HL1001. 2010, 1–12

Download references

Acknowledgements

This work was supported by the Foundation for Distinguished Young Scholars of Fujian Agriculture and Forestry University (xjq201809), MOST of Taiwan (107-2623-E-009-006-D).

Author information

Authors and Affiliations

  1. College of Computer and Information Sciences, Digital Fujian Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Xiaolong Liu, Jinchao Liang & Riqing Chen

  2. Department of Computer Science, Chiao Tung University, Hsinchu, 300, China

    De-Yu Liu & Shyan-Ming Yuan

Authors
  1. Xiaolong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinchao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. De-Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Riqing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shyan-Ming Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiaolong Liu or Shyan-Ming Yuan.

Additional information

Xiaolong Liu received his BS degree from Xiamen University, China in 2011, his MS degree in Computer Science and Information Management from Providence University, China in 2013, his PhD degree in Institute of Computer Science and Engineering, Chiao Tung University, China in 2016. Since 2016, he has been an Associate Professor with the College of Computer and Information Sciences, Fujian Agriculture and Forestry University, China. He is the author of more than 40 peer-reviewed international journal and conference papers. His research interests include distributed computing, artificial intelligence and software security.

Jinchao Liang is now studying for his BS degree from College of Computer and Information Sciences, Fujian Agriculture and Forestry University, China. His current research interests include distributed computing, artificial intelligence.

De-Yu Liu received his BS degree and MS from Institute of Computer Science and Engineering, Chiao Tung University, China in 2016 and 2019, respectively. His current research interests include distributed computing, artificial intelligence.

Riqing Chen received the BEng degree in communication engineering from Tongji University, China in 2001, the MSc degree in communications and signal processing from Imperial College London, UK in 2004, and the DPhil degree in engineering science from the University of Oxford, UK in 2010. Since 2014, he has been a professor with Fujian Agriculture and Forestry University, China. His current research interests include cloud computing, wireless modulation, and transmission security.

Shyan-Ming Yuan received his PhD degree in Computer Science from the University of Maryland, USA in 1989. Since 1995, he has been a professor at the Department of Computer Science, Chiao Tung University, China. He is also the Director of Library at Chiao Tung University, China. He has authored or co-authored over 200 peer-reviewed papers. His current research interests include distance learning, internet technologies and distributed computing.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Liang, J., Liu, DY. et al. Weapon-target assignment in unreliable peer-to-peer architecture based on adapted artificial bee colony algorithm. Front. Comput. Sci. 16, 161103 (2022). https://doi.org/10.1007/s11704-021-0395-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-021-0395-8

Keywords

Search

Navigation