Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

An Efficient Genetic Algorithm for the Allocation and Engagement Scheduling of Interceptor Missiles

효율적인 유전 알고리즘을 활용한 요격미사일 할당 및 교전 일정계획의 최적화

  • Lee, Dae Ryeock (Department of Business Administration, University of Seoul/Korea Institute of Marine Science and Technology Promotion) ;
  • Yang, Jaehwan (Department of Business Administration, University of Seoul)
  • 이대력 (서울시립대학교 경영학과/한국해양과학기술진흥원) ;
  • 양재환 (서울시립대학교 경영학부)
  • Received : 2016.03.30
  • Accepted : 2016.06.09
  • Published : 2016.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

Keywords

References

  1. Ahner, D.K. and Parson, C.R., Optimal multi-stage allocation of weapons to targets using adaptive dynamic programming, Optimization Letters, 2015, Vol. 9, No. 8, pp. 1689-1701. https://doi.org/10.1007/s11590-014-0823-x
  2. Ahuja, R.K., Kumar, A., Jha, K.C., and Orlin, J.B., Exact and heuristic algorithms for the weapon-target assignment problem, Operations Research, 2007, Vol. 55, No. 6, pp. 1136-1146. https://doi.org/10.1287/opre.1070.0440
  3. Chen, H., Liu, Z., Sun, Y., and Li, Y., Particle swarm optimization based on genetic operators for sensor-weapon-target assignment, 2012 Fifth International Symposium on Computational Intelligence and Design, 2012, pp. 170-173.
  4. Chen, J., Xin, B., Peng, Z.H., Dou, L.H., and Zhang, J., Evolutionary decision-makings for the dynamic weapontarget assignment problem, Science in China Series F : Information Sciences, 2009, Vol. 52, No. 11, pp. 2006-2018. https://doi.org/10.1007/s11432-009-0190-x
  5. Coello, C.A., Theoretical and numerical constraint- handling techniques used with evolutionary algorithms : a survey of the state of the art, Computer Methods in Applied Mechanics and Engineering, 2002, Vol. 191, pp. 1245-1287. https://doi.org/10.1016/S0045-7825(01)00323-1
  6. Fu, T., Liu, Y., and Chen, J., Improved genetic and ant colony optimization algorithm for regional air defense WTA problem, Proceedings of the First International Conference on Innovative Computing, Information and Control, 2006, pp. 226-229.
  7. Geetha, S., A hybrid particle swarm optimization with genetic operators for vehicle routing problem, Journal of Advances in Information Technology, 2010, Vol. 1, No, 4, pp. 181-188.
  8. Gulez, T., Weapon-target allocation and scheduling for air defense with time varying hit probabilities [master's thesis]. [Ankara, Turkey] : Middle East Technical University, 2007.
  9. Hong, S.S., Han, M.M., Choi, H.J., and Mun, C.M., A study of population initialization method to improve a genetic algorithm on the weapon target allocation problem, Journal of Korean Institute of Intelligent Systems, 2012, Vol. 22, No. 5, pp. 540-548. https://doi.org/10.5391/JKIIS.2012.22.5.540
  10. Hosein, P., A class of dynamic nonlinear resource allocation problems [dissertation], [Cambridge, Massachusetts, United States] : Massachusetts Institute of Technology, 1989.
  11. Jang, J.G., Kim, K., Choi, B.W., and Suh, J.J., A linear approximation model for an asset-based weapon target assignment problem, Journal of Society of Korea Industrial and Systems Engineering, 2015, Vol. 38, No. 5, pp. 108-116.
  12. Julstrom, B.A., String-and permutation-coded genetic algorithms for the static weapon-target assignment problem, Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, 2009, pp. 2553-2558.
  13. Jung, H.S., Ballistic Missile Defense, Journal of the Defense Science and Technology Information, 2013, Vol. 40, p. 169.
  14. Karasakal, O., Optimal air defense strategies for a naval task group [dissertation], [Ankara, Turkey] : Middle East Technical University, 2004.
  15. Khosla, D., Hybrid genetic approach for the dynamic weapon-target allocation problem, Proceedings of SPIE, 2001, Vol. 4396, pp. 244-259.
  16. Kim, M.S., The missiles that occupy news, KAMD and Kill Chain, Geunduun, 2013, Vol. 59, pp. 14-17.
  17. Krokhmal, P., Murphey, R., Pardalos, P., and Uryasev, S., Use of conditional value-at-risk in stochastic programs with poorly defined distributions, S. Butenko et al.(Eds.). Recent Developments in Cooperative Control and Optimization, Kluwer Academic Publishers, 2004, pp. 225-243.
  18. Lee, D.R. and Yang, J., A study on the allocation and engagement scheduling of air defense missiles by using mixed integer programming, Korean Management Science Review, 2015, Vol. 32, No. 4, pp. 109-133. https://doi.org/10.7737/KMSR.2015.32.4.109
  19. Lee, J.B., A study on real time dynamic multi-weapon multi-target assignment algorithm [dissertation]. [Taejeon, Korea] : Korea Advanced Institute Science and Technology, 2009.
  20. Lee, M.Z., Constrained weapon-target assignment : enhanced very large scale neighborhood search algorithm, IEEE Transactions on Systems Man and Cybernetics-Part A Systems and Humans, 2010, Vol. 40, No. 1, pp. 198-204.
  21. Lee, Z.J., Lee, C.Y., and Suc, S.F., An immunity-based ant colony optimization algorithm for solving weapon- target assignment problem, Applied Soft Computing, 2002, Vol. 2, No. 1, pp. 39-47. https://doi.org/10.1016/S1568-4946(02)00027-3
  22. Liu, C., Wang, H., and Qiu Z., An adaptive memetic algorithm solving dynamic weapon target assignment problem, IEEE International Conference on Information Engineering and Computer Science, 2010, pp. 1-4.
  23. Liu, P., Xiong, J., and Zhang, W., WTA model study of air defense missile system based on particle algorithm, Proceedings of IEEE Chinese Guidance, Navigation and Control Conference, Yantai, China, 2014, pp. 1534-1538.
  24. Ministry of National Defense(Republic of Korea), Defense White Paper, 2014, pp. 58-59.
  25. Moon, B.R., Easy Learning Genetic Algorithms. Seoul, Korea : Hanbit Media Inc., 2008, pp. 59-61.
  26. Murphey, R.A., "An approximate algorithm for a weapon target assignment stochastic program," in Approximation and Complexity in Numerical Optimization : Continuous and Discrete Problems, Kluwer Academic Publishers, 1999, pp. 1-16.
  27. Song, Z., Zhu, F., and Zhang, D., A heuristic genetic algorithm for solving constrained weapon-target assignment problem, IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009, pp. 336-341.
  28. Teng, P., Lv, H., Huang, J., and Sun, L., Improved particle swarm optimization algorithm and its application in coordinated air combat missile-target assignment, Proceedings of the 7th World Congress on Intelligent Control and Automation, 2008, pp. 2833-2837.
  29. Woo, B.-H., Reinforced Genetic Algorithm for Solving Reliability Optimization Design Problem, Journal of the Korean Institute of Plant Engineering, 2012, Vol. 17, No. 4, pp. 17-33.
  30. Xin, B. and Chen. J., An Estimation of Distribution Algorithm with Efficient Constructive Repair/Improvement Operator for the Dynamic Weapon-Target Assignment, Proceedings of the 31st Chinese Control Conference, Hefei, China, 2012, pp. 2346-2351.
  31. Xin, B., Chen J., Peng, Z., Dou, L., and Zhang, J., An efficient rule-based constructive heuristic to solve dynamic weapon-target assignment problem, IEEE Transactions on Systems, Man, and Cybernetics-Part A : Systems and Humans, 2011, Vol. 41, No. 3, pp. 598-606. https://doi.org/10.1109/TSMCA.2010.2089511
  32. You, H.-S., A performance improvement study on weapon assignment of fire control system [master's thesis], [Seoul, Korea] : Hanyang University, 2014.
  33. Zhang, J., Xu, C., Wang, X., and Yuan, D., ACGA algorithm of solving weapon-target assignment problem, Open Journal of Applied Sciences, 2012, Vol. 2, No. 4, pp. 74-77.
  34. Zhu, B., Zou, F., and Wei, J., A novel approach to solving weapon-target assignment problem based on hybrid particle swarm optimization algorithm, International Conference on Electronic and Mechanical Engineering and Information Technology, 2011, pp. 1385-1387.

Cited by

  1. 양자화 유전자알고리즘을 이용한 무기할당 vol.40, pp.4, 2016, https://doi.org/10.11627/jkise.2017.40.4.260
  2. 동적 무장할당 문제에서의 GRASP 알고리즘 연구 vol.47, pp.12, 2016, https://doi.org/10.5139/jksas.2019.47.12.856