요격미사일 배치문제에 대한 하이브리드 유전알고리듬 적용방법 연구

An Application of a Hybrid Genetic Algorithm on Missile Interceptor Allocation Problem

  • 한현진 (육군 제3군수지원사령부)
  • Han, Hyun-Jin (The 3rd Logistics Support Command, Republic of Korea Army)
  • 발행 : 2009.12.31

초록

A hybrid Genetic Algorithm is applied to military resource allocation problem. Since military uses many resources in order to maximize its ability, optimization technique has been widely used for analysing resource allocation problem. However, most of the military resource allocation problems are too complicate to solve through the traditional operations research solution tools. Recent innovation in computer technology from the academy makes it possible to apply heuristic approach such as Genetic Algorithm(GA), Simulated Annealing(SA) and Tabu Search(TS) to combinatorial problems which were not addressed by previous operations research tools. In this study, a hybrid Genetic Algorithm which reinforces GA by applying local search algorithm is introduced in order to address military optimization problem. The computational result of hybrid Genetic Algorithm on Missile Interceptor Allocation problem demonstrates its efficiency by comparing its result with that of a simple Genetic Algorithm.

키워드

참고문헌

  1. Bazaara, M.S. and Elshafei, A.N., 1979, An Exact Branch-and-Bound Procedure for Quadratic Assignment Problem, Naval Research Logistics Quarterly 27 Vol.1, pp.109-120
  2. Heffley, D.R., 1972. The Quadratic Assignment Problem: A Note. Economerica, VoL.40, No.6, pp.1155-1163
  3. Jewell, W.S., 1977, The Analytic Methods of Operations Research, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, Vol. 287, No.1346, pp.373-404
  4. Kim, C. Y., 2000, Nonlinear Programming, Du-Nam Publications, Seoul, Korea
  5. Koopmans, T.C. and Beckmann, M.J., 1957, Assignment Problems and the Location of Economic Activities, Econometrica 25, pp.53-76 https://doi.org/10.2307/1907742
  6. Lawler, E.L., 1963, The Quadratic Assignment Problem, Management Science 9, pp.586-599 https://doi.org/10.1287/mnsc.9.4.586
  7. Michalewicz, Zbigniew, 1996, Genetic Algorithms + Data Structures = Evolution Programs, 3rd Edition, Springer–Verlag Berlin Heidelberg, New York
  8. Reeves, C.R., 1993, Modern Heuristic Techniques for Combinatorial Problems, Blackwell Scientific Publications, London, UK
  9. Taillard, E., 1991, Robust taboo search for the quadratic assignment problem, Parallel Computing 19, pp.443-455
  10. Tate, D.M. and Smith, A.E., 1995, A Genetic Approach to The Quadratic Assignment Problem, Computers Operations Research, Vol.22, No.1, pp.73-83 https://doi.org/10.1016/0305-0548(93)E0020-T
  11. Wilhelm, M.R. and Ward, T.L., 1987, Solving Quadratic Assignment Problems by 'Simulated Annealing', IIE Transactions, Vol.19, March, pp.107-119 https://doi.org/10.1080/07408178708975376