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전투실험 분석을 위한 최적화 시뮬레이션 프레임워크

Optimized Simulation Framework for the Analysis in Battle systems

  • 강종구 ;
  • 이민규 (국방과학연구소 제6기술연구본부) ;
  • 김선범 (국방과학연구소 제6기술연구본부) ;
  • 황근철 (국방과학연구소 제6기술연구본부) ;
  • 이동훈 (국방과학연구소 제6기술연구본부)
  • 투고 : 2014.10.21
  • 심사 : 2015.06.01
  • 발행 : 2015.06.30

초록

다양한 변수들이 존재하는 현대의 전투전장에서는 운용전술에 따라 전투의 양상이 결정되기 때문에 최적화된 운용전술을 도출하는 연구가 필요하다. 기존의 M&S(Modeling & Simulation) 연구에서는 몬테 칼로 실험을 통해 변수들을 분석하는 것이 일반적이다. 그러나 이 방법은 상호 복합적으로 작용하는 다수의 변수들의 모든 조합에 대해 시뮬레이션을 수행하기 때문에, 많은 수행시간이 소요되고 최적의 운용전술 도출을 위한 별도의 분석이 필요하다. 본 논문에서는 최적화 요소를 찾는 전산탐색 기법 중 하나인 DPSO(Discrete binary version of PSO) 알고리즘을 기반으로 하는 최적화 시뮬레이션 프레임워크를 제안하였다. 최적화 시뮬레이션 프레임워크는 짧은 시간 내에 최적화된 운용전술을 도출하기 위하여 설계되었다. 본 연구에서는 아군 수상함이 적 어뢰로부터 회피하는 사례를 적용하여 최적화 시뮬레이션 프레임워크의 탐색 성능을 확인하였다. 이를 통해 최적화 시뮬레이션 프레임워크의 효율성을 제시하였다.

The tactical employment is a critical factor to win the war in the modern battlefield. To apply optimized tactics, it needs analyses related to a battle system. Normally, M&S (Modeling & Simulation) has been studied to analyze data in general problems. However, this method is not suitable for military simulations because there are many variables which make complex interaction in the system. For this reason, we suggested the optimized simulation framework based on the M&S by using DPSO (Discrete binary version of PSO) algorithm. This optimized simulation framework makes the best tactical employment to reduce the searching time compared with the normal M&S used by Monte Carlo search method. This paper shows an example to find the best combination of anti-torpedo scenario in a short searching time. From the simulation example, the optimized simulation framework presents the effectiveness.

키워드

참고문헌

  1. Koh, W., "War paradigm shift and its implications for korea military operation", The Quarterly Journal of Defense Policy studies, Vol. 26, No. 4, pp. 9-46, 2010.12.
  2. Hwang, K.C., "The anti air warfare analysis & design of the patrol killer experiment combat system by the model-based-simulation", Journal of the Korea Society for Simulation, Vol. 16, No. 4, pp. 23-31, 2007.12.
  3. Lee, D.H. and Huh, S.P., "Genetic algorithm and clustering technique for optimization of stochastic simulation", Journal of the Korea Institute of Military Science and Technology, Vol. 2, No. 1, pp. 90-100, 1999.6.
  4. Lee, Y.H., Cho, K.T., Lee, S.Y., Hwang, K.C. and Kim S.H. "Framework and tools for rapid M&S component development and reusability", Journal of the Korea Society for Simulation, Vol. 22, No. 4, pp. 29-38, 2013.12. https://doi.org/10.9709/JKSS.2013.22.4.029
  5. Hwang, K.C., Lee, M.K., Han S.J., Yoon J.M., You Y.J., Kim, S.B., Nah, Y.I., Kim J.H. and Lee, D.H., "The DEVS integrated development environment for simulation-based battle experimentation", Journal of the Korea Society for Simulation, Vol. 22, No. 4, pp. 39-47, 2013.12. https://doi.org/10.9709/JKSS.2013.22.4.039
  6. J. Kennedy and R. Eberhart, "Particle swarm optimization", IEEE International Conference, pp. 1942-1948, 1997.
  7. Yim, D.S., "Particle swarm optimizations to solve multi-valued discrete problems", Society of Korea Industrial and Systems Engineering, Vol. 36, No. 3, pp. 63-70, 2013.9.
  8. Nah, Y.I., Lee, S.Y., Yoon, H.S., "The development of torpedo defense experimental technique based on M&S", Journal of the Korea Institute of Military Science and Technology, Vol. 13, No. 5, pp. 818-823, 2010.10.
  9. Kim, S.B., Kim, J.H., and Lee, D.H., "A study of modified PSO for the optimization of stochastic simulations", Journal of the Korea Society for Simulation, Vol. 22, No. 4, pp. 21-28, 2013.12. https://doi.org/10.9709/JKSS.2013.22.4.021
  10. Lee, S.Y., Jang, S.H., Lee, J.S., "Modeling and simulation of optimal path considering battlefield-situation in the war-game simulation", Journal of the Korea Society for Simulation, Vol. 19, No. 3, pp. 27-35, 2010.9.
  11. Han, S.J., Lee, M.K., " Development of underwater warfare models on the naval weapon systems", Journal of the Korea Society for Simulation, Vol. 22, No. 4, pp. 1-9, 2013.12. https://doi.org/10.9709/JKSS.2013.22.4.001
  12. J. Kennedy, "The behavior of particles", Evolutionary Programming VII, Lecture Notes in Computer Science, Vol. 1447, pp. 579-589, 1998.
  13. J. Kennedy and R. Eberhart, "A discrete binary version of the particle swarm algorithm", IEEE International Conference on Systems, pp. 4104-4108, 1997.
  14. Kang, J.H., Lee, S.J., Cha, J.H., Yoo, S.J., Lee, H.K., Lee, K.Y., Kim, T.W., Ko, Y.S., "A simulation for the analysis of the evasive capability of submarine against a torpedo using DEVS modeling", Journal of the Korea Society for Simulation, Vol. 14, No. 2, pp. 57-71, 2005.6.