| 研究生: |
趙世俊 SHIH-CHUN CHAO |
|---|---|
| 論文名稱: |
一種滿足損傷條件的火力分配模型及算法 Weapon-target assignment model of Artillery satisfying expected damage probabilities and algorithm |
| 指導教授: |
李允中
Jonathan Lee |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
資訊電機學院 - 資訊工程學系在職專班 Executive Master of Computer Science & Information Engineering |
| 畢業學年度: | 100 |
| 語文別: | 中文 |
| 論文頁數: | 54 |
| 中文關鍵詞: | 資源分配問題 、矩陣極值篩選法 、火力分配 、目標武器分配 、多目標最佳化 |
| 外文關鍵詞: | weapon target assignment, Resource Allocation Problem, fire distribution, multi-objective optimization, matrix maximal filter algorithm |
| 相關次數: | 點閱:9 下載:0 |
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現代高科技戰爭的條件下存在著大量火力分配問題,而這些問題往往會耗費很多計算資源和時間,在效率上無法滿足需求。為了求得火力分配問題的全域最佳解,所需要的計算時間將隨著問題規模的增加而呈指數倍增,火力分配問題的基本特性說明想在很短時間內解出大量複雜火力分配問題的全域最佳解並不切實際,只能求其滿意解或可行解。本論文主要研究對特定族群在火力分配問題上所量身訂做的一套演算法,在滿足損傷條件下,成功地在短時間內求解大量複雜的火力分配問題,並使用效益評估效能指標輔助決策者選擇較佳火力分配方案。
火力分配問題同時也是多目標最佳化問題,其最佳解通常為一組解集合,解集合中的元素就所有目標而言是彼此間不可比較的。然而,最終只需要一個解,為了找出滿足需求的解,研究中發現沒有單一個別的演算法是最佳方法,相反的,在不同的問題模型層次中有不同的解決方法,取決於使用者需求、使用者偏好和實際的應用面。火力分配問題隨著武器數及目標數的增加,其解將呈現組合爆炸性的趨勢,當問題的規模變大時,包含更多的決策變數和限制條件,為了求得全域最佳解往往耗費相當漫長的計算時間。因此,研究火力分配問題,特別是多武器多目標的大量火力分配問題,尋求較佳的武器目標分配的快速解決方案,提高作戰效能,是當前指揮管制問題中的一個重要課題。
There are many problems of fire distribution in modern high tech war, which can often be abstracted to multi-objective optim- ization problems. The mathematics properties of fire distribution mean that obtain optimal global solutions usually impossible in limited time , and the near optimal solutions are thought.The matrix maximal filter algorithm is proposed in the paper, which is used to solve the fire distribution problem with high performance . The measure of effectiveness is designed for comparison of course of actions and could be a good conception of selecting better fire plans.
It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem,the user’s preferences, the solution requirements, and the availability of application.The fire distribution problem will become more complicated with the increasing of the number of weapons and targets , there are also include more decision parameters and limited conditions .The solution of optimization can not be guaranteed for the evolutionary algorithms,but consuming many resources and waste much time for computing . Therefor, the study of weapon-target assignment with high performance especial for the multi-object optimization is a key part in the process of command decision making.
[1] Kalyanmoy Deb* ,Kanpur Genetic Algorithms Laboratory(KanGAL), “ Multi-objective Genetic Algorithms:Problem Difficulties and Construction of Test Problems.Department of Mechanical Engineer ing “,Indian Institute of Technolory Kanpur,1998
[2] R.T. Marler and J.S. Arora,”Survey of Multi-objective Optimization Methods for Engineering.” Struct Multidisc Optim 26, 369–395 ,2004
[3] M.Alper SAHIN TUBITAK-UEKAE ILTAREN,”A Standard Expert System For Weapon Target Assignment Problem.”Department of Electrical and Electronics Engineering,METU,Ankara,TURKEY,SPECTS 2009
[4] Gao Shang .”Solving Weapon-Target Assignment Problems by a New Colony Algorithm .” Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310027, International Symposium on Computational Intelligence and Design.2008 IEEE
[5] Cai Huaiping,Liu Jingxu,Chen Yingwu& Wang Hao.”Survey of reseach on dynamic weapon-target assignment problem.”IEEE Journal of Systems Engineering and Electronics ,559~565,2006
[6] Lloyd S P, H S W. “Weapons Allocation is NP-complete [A].” Proceedings of the IEEE Summer Simulation Conference[C]. Reno, Nevada. 1986.
[7] Christos H.Papadimitriou.”NP-completeness A retrospective . “ University of California,Berkeley,USA
[8] Li Zenghua .“ Weapon-Target Assignment Research Based on Genetic Algorithm Mixed with Damage Simulation.”2010 International Conference on Computer Application and System Modeling(iccasm 2010)
[9] Mei-Zi Lee.”Constrained Weapon–Target Assignment: Enhanced Very Large Scale Neighborhood Search Algorithm.”IEEE TRANSACTIONS ON SYSTEM,MAN,AND CYBERNETICS-PART A:SYSTEMS AND HUMANS ,VOL 40 , NO.1,JANUARY 2010 .
[10] Deb K Pratsp A, Agatwal S etal ,”A fast and elitist multi -objective genetic algorithm :NSGA-II “.IEEE Transactions on Evolutionary Computation 2002 .
[11] Zitzler E ,Thiele L .” Multiobjective evolutionary algorithms :A comparative case study and the strength pareto approach .” IEEE Transactions on Evolutionary Computation , 1999 .3
[12] Zitzler E ,Thiele L Laumanns Metal .”Performance assessment of multi-objective optimizers :An analysis and review .” IEEE Transactions on Evolutionary Computation,2003 .
[13] K. C. Tan, T. H. Lee and E. F. Khor ,“Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons” Department of Electrical and Computer Engineering National University of Singapore ,Proceedings of the 2001 IEEE Congress on Evolutionary Computation .Seoul,Korea .
[14] Deb k ,Agrawal S,Pratap A and Meyarivan T .”A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization :NSGA “ ILPamllel Problem Solving from Nature (PPSN VI) ,Berlin ,2000 .
[15] Lina Chen, ChuanJun Ren, Su Deng“An Efficient Approximation for Weapon-Target Assignment” National University of Defense Technology, ChangSha, HuNan, 410073, China . ISECS International Colloquium on Computing, Communication, Control, and Manage- ment.2008 IEEE .
[16] Intelligence preparation of the battlefield (chapter 6 intelligence preparation of the battlefield for operations other then war)
[17] Staff organization and operations (chapter 5 military decision-making process)