Performance Comparison of Heuristics for Weapon-Target Assignment Problem with Transitivity Rules in Weapon's Kill Probability

무장 할당문제에서 휴리스틱 방법 효율성 비교: 이행성 규칙이 성립하는 무장성능차이를 중심으로

  • 임동순 (한남대학교 산업경영공학과) ;
  • 최봉완 (한남대학교 국방전략대학원/무기체계.M&S 연구센터)
  • Received : 2010.09.09
  • Accepted : 2010.11.29
  • Published : 2010.12.31

Abstract

In this study, the weapon-target assignment problem arising in military application of operations research is considered. We reformulated the problem in order to simplify the solution methods based on genetic algorithms and heuristics. Since the problem is well known as NP-complete and cannot be solved in polynomial time, such solution methods have been widely used to obtain good solutions. Two chromosome representations--target number representation and permutation representation--in genetic algorithm are compared. In addition, a construction heuristic and three improving heuristics are developed. Several experiments under the condition of transitivity rules in weapon's kill probability have been accomplished. It shows that the construction heuristic and exchange-based improving heuristic guarantees good solutions within a second and the performance of construction heuristic is sensitive to transitivity rules.

운용과학의 군사 응용 분야 중 하나인 무장-표적 할당문제는 NP-complete 문제로 알려져 있어 주어진 시간내에 최적해를 구할 수 없으므로 휴리스틱 방법에 의해 빠른 시간 내에 우수한 해를 구하는 것이 더 의미가 있다. 본 연구에서는 보다 효율적으로 해를 도출할 수 있는 방법을 개발하기 위해 전형적인 문제를 재구성하여 단순화 시켰다. 이러한 문제 하에서 두가지 유전자 알고리즘인 표적번호 표현 방법과 순열 표현방법을 비교하였고, 구성적 휴리스틱, 향상적 휴리스틱들을 개발하여 비교하였다. 무장의 파괴확률 간에 이행성 규칙이 존재하는 경우를 대상으로 실험을 수행한 결과 구성적 휴리스틱의 해를 초기해로 하여 교환에 기초한 향상적 계산 시간이나 해의 질 측면에서 가장 우수한 해를 생성하였다. 그러나, 구성적 휴리스틱의 효율성은 무장 성능 간 이행성 규칙에 민감한 결과를 나타내었다.

Keywords

Acknowledgement

Supported by : 한남대학교

References

  1. Ahuja, R. K., Kumar, A., Jha, K. C., and Orlin, J. B., "Exact and Heuristic Algorithms for the Weapon-Target Assignment Problem", Operations Research, Vol. 55. No. 6, pp. 1136-1140, 2007. https://doi.org/10.1287/opre.1070.0440
  2. Davis, L., "Applying Adaptive Algorithms to Epistatic Domains", Proceedings of the International Joint Conference on Artificial Intelligence, 162-164, 1985.
  3. Goldberg, D. E. and Lingle, R., "Alleles, Loci, and the TSP", Proceedings of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Hillsdale, NJ, 154-159, 1985.
  4. Harhalakis, G., Proth, J. M., Xie, X. L.,"Manufacturing cell design using simulated annealing: An industrial application", Journal of Intelligent Manufacturing, pp. 185-191, 1990.
  5. Jones, D. R. and Bertramo, M. A., "Solving Partitioning Problems with Genetic Algorithms", Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 442-449, 1991
  6. Lee, Z. J., Fu, S. F., and Lee, C. Y., "Efficiently Solving General Weapon-Target Assignment Problem by Genetic Algorithms With Greedy Eugenics", IEEE Transactions on Systems, man, and cybernetics, Vol. 33, No. 1, pp. 113-121, 2003 https://doi.org/10.1109/TSMCB.2003.808174
  7. Lloyd, S. P. and Witsenhausen, H. S., "Weapon Allocation is NP-complete", Proc. 186 Summer Conf. Simulation, Reno, NV, 1054-1058, 1986
  8. Michaelwicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 1992.
  9. Murphey, R. A., "Target-based weapon target assignment problems", P. M. Pardalos, L. S. Pitsoulis, eds, Nonlinear Assignment Problems: Algorithms and Applications, KIuwer Academic Publishers, Boston, MA. 39-53, 1999
  10. Oliver, I. M., Smith, D. J., and Holland, J. R. C., "A Study of Permutation Crossover Operators on the traveling Salesman Problem", Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Hillsdale, NJ, 224-230, 1987.