基于遗传算法的空战编队优化研究
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摘要
本论文主要进行两方面的理论研究：一方面是发展一种用遗传算法优化大规模空战编队战术的方法；另一方面是对遗传模拟退火算法的寻优性能进行研究。
首先，本论文发展了一种用遗传算法优化大规模空战编队战术的方法，并用模拟退火算法对其进行了改进，使得收敛性能大大提高。该方法是借鉴层级编队思想，对多机编队实行层级编码，在一定的空战模型的基础上，用遗传算法优化交战结果，最终得到一个对给定编队作战的最优编队。关键点在于：①超视距作战用战绩来反映交战结果和编队优势；②用遗传算法来进行基于战绩的优化。该方法可为分析现有空战编队的优势和挖掘新的空战编队提供参考。最后，用两个16机编队作战的算例验证了该方法的有效性。
其次，本论文对遗传模拟退火算法的寻优性能进行了研究。遗传算法是一种模拟生物在自然环境中的遗传和进化过程而形成的一种自适应全局优化概率搜索算法。该方法与常规优化算法相比最大的优点是能处理离散变量的优化问题。本文从理论上阐述了遗传算法、模拟退火算法和遗传模拟退火算法的生物基础和构成要素，分析了它们的寻优速度、收敛能力等特性。最后，用计算试验对比了遗传算法和遗传模拟退火算法的收敛性能，对比了采用不同状态函数和不同Markov链的遗传模拟退火算法的收敛性能，并对结果进行了分析。
On one hand, the optimization method for large-scale air combat formation tactics based on genetic algorithm is developed in this paper. A hierarchical formation tactics is employed in this method and the hierarchical code is introduced to encode formations. Then genetic algorithm improved by simulated annealing algorithm is used to optimize the engagement results of BVR (Beyond View Range) intercepts so that a better formation can be gained. This paper integrates two key ideas. The first is that performance reflected engagement outcome and formation advantage, and the second is using genetic algorithm for performance-based optimization of blue team formation tactics. This method may act as a reference for analyzing the advantage of formations and exploring new formations. At last two calculating examples are used to testify the effectiveness of this method.
On the other hand, the property of genetic algorithm improved by simulated annealing algorithm for finding the best value is studied in this paper. Genetic algorithm is an adaptive global optimal algorithm that simulates the inheritance and evolution of the biology. The whole course of finding the best value depends on probability. It differs from other traditional optimal algorithms mainly in dealing with discrete points. Genetic algorithm, simulated algorithm and genetic algorithm improved by simulated annealing algorithm are described and their abilities of finding the best value are analyzed in this paper. At last, several examples of genetic algorithm improved by simulated annealing algorithm, which adopted different functions deciding whether to accept a state and different Markov chains, are used to analyze the property of this algorithm.
首先，本论文发展了一种用遗传算法优化大规模空战编队战术的方法，并用模拟退火算法对其进行了改进，使得收敛性能大大提高。该方法是借鉴层级编队思想，对多机编队实行层级编码，在一定的空战模型的基础上，用遗传算法优化交战结果，最终得到一个对给定编队作战的最优编队。关键点在于：①超视距作战用战绩来反映交战结果和编队优势；②用遗传算法来进行基于战绩的优化。该方法可为分析现有空战编队的优势和挖掘新的空战编队提供参考。最后，用两个16机编队作战的算例验证了该方法的有效性。
其次，本论文对遗传模拟退火算法的寻优性能进行了研究。遗传算法是一种模拟生物在自然环境中的遗传和进化过程而形成的一种自适应全局优化概率搜索算法。该方法与常规优化算法相比最大的优点是能处理离散变量的优化问题。本文从理论上阐述了遗传算法、模拟退火算法和遗传模拟退火算法的生物基础和构成要素，分析了它们的寻优速度、收敛能力等特性。最后，用计算试验对比了遗传算法和遗传模拟退火算法的收敛性能，对比了采用不同状态函数和不同Markov链的遗传模拟退火算法的收敛性能，并对结果进行了分析。
On one hand, the optimization method for large-scale air combat formation tactics based on genetic algorithm is developed in this paper. A hierarchical formation tactics is employed in this method and the hierarchical code is introduced to encode formations. Then genetic algorithm improved by simulated annealing algorithm is used to optimize the engagement results of BVR (Beyond View Range) intercepts so that a better formation can be gained. This paper integrates two key ideas. The first is that performance reflected engagement outcome and formation advantage, and the second is using genetic algorithm for performance-based optimization of blue team formation tactics. This method may act as a reference for analyzing the advantage of formations and exploring new formations. At last two calculating examples are used to testify the effectiveness of this method.
On the other hand, the property of genetic algorithm improved by simulated annealing algorithm for finding the best value is studied in this paper. Genetic algorithm is an adaptive global optimal algorithm that simulates the inheritance and evolution of the biology. The whole course of finding the best value depends on probability. It differs from other traditional optimal algorithms mainly in dealing with discrete points. Genetic algorithm, simulated algorithm and genetic algorithm improved by simulated annealing algorithm are described and their abilities of finding the best value are analyzed in this paper. At last, several examples of genetic algorithm improved by simulated annealing algorithm, which adopted different functions deciding whether to accept a state and different Markov chains, are used to analyze the property of this algorithm.
引文
1. Mulgund, S., Harper, K., Krishnakumar, K. and Zacharias, G.. Air Combat Tactics Optimization using Stochastic Genetic Algorithms. Proc. of 1998 IEEE Intl. Conference on Systems, Man, and Cybernetics, (1998), pp.3136-3141.
2. Mulgund, S., Harper, K., Krishnakumar, K. and Zacharias, G... Large-Scale Air Combat Tactics Optimization Using Genetic Algorithms. AIAA Journal of Guidance, Control and Dynamics, 24(1), pp. 140-142, (Jan.-Feb.)
3. Krishnakumar, K., Swaminathan, R., Garg, S. and Narayanaswamy, S.. Solving Large Parameter Optimization Problems using Genetic Algorithms. Proc. of the Guidance, Navigation, and Control Conference, Baltimore, MD, 1995
4．周明，孙树栋．遗传算法原理及应用．北京：国防工业出版社，1999
5. Holland J H. Adaptation in Nature and Artificial Systems. MIT Press, 1992
6. De Jong K A. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D Dessertation, University of Michgan, No.76-9381, 1975
7. Goldberg D E. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989
8. Grefenstette J J. Incorporating Problem Specific Knowledge into Genetic Algorithms. In: Davis L Ed. Genetic Algorithms and Simulated Ammealing, Pitman, 1987, 42～60
9. Orvosh D, Davis L. Using a Genetic Algorithm to Optimize Problems with Feasibility Constraints. In: Proc. of 1 st IEEE Conf. On Evolutionary Computation, 1994, 548～553
10. Chen H, Flann N S. Parallel Simulated Annealing and Genetic Algorithms: A Space of Hybrid Methods. In: Parallel Problem Solving
from Nature 3, Springer-Verlag, 1994, 428～438
11．李建军．超视距多机协同空战分析．西北工业大学硕士学位论文，2001
12．康立山等．非数值并行算法——模拟退火算法．北京：科学出版社，1994
13．王凌，郑大钟．基于Cauchy和Gaussian分布状态发生器的模拟退火算法．清华大学学报(自然科学版)，2000，40(9)：109-112
14. Dueck, G., Scheuer, T.. Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing[J]. J Comput Phys, 1990, 90: 161-175
15．程国采．战术导弹导引方法．北京：国防工业出版社，1996
16．李廷杰．导弹武器系统的效能及其分析．北京：国防工业出版社，2000
17．朱宝鎏，朱荣昌，熊笑非．作战飞机效能评估．北京：航空工业出版社，1993
18．李林森，佟明安．协同多目标攻击空战决策及其神经网络实现．航空学报，1999，20(4)：309-312
19．蓝伟华，赵春玲．空对空多机协同攻击多个目标的战术决策研究．火力与指挥控制，1999，24(1)：30-33
20．谢希权，李伟仁．单机多目标攻击逻辑的对策性决策．系统工程与电子技术，2000，22(7)：28-31
21．王红等．一种多目标攻击逻辑与决策方法．兵工学报，1997，18(3)：244-247
22．王宏伦，佟明安．多机空战仿真系统．西北工业大学学报，1998，16(1)：70-74
23．谭浩强．C程序设计．北京：清华大学出版社，1991
24．郑莉等．C++语言程序设计．北京：清华大学出版社，2000
2. Mulgund, S., Harper, K., Krishnakumar, K. and Zacharias, G... Large-Scale Air Combat Tactics Optimization Using Genetic Algorithms. AIAA Journal of Guidance, Control and Dynamics, 24(1), pp. 140-142, (Jan.-Feb.)
3. Krishnakumar, K., Swaminathan, R., Garg, S. and Narayanaswamy, S.. Solving Large Parameter Optimization Problems using Genetic Algorithms. Proc. of the Guidance, Navigation, and Control Conference, Baltimore, MD, 1995
4．周明，孙树栋．遗传算法原理及应用．北京：国防工业出版社，1999
5. Holland J H. Adaptation in Nature and Artificial Systems. MIT Press, 1992
6. De Jong K A. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D Dessertation, University of Michgan, No.76-9381, 1975
7. Goldberg D E. Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989
8. Grefenstette J J. Incorporating Problem Specific Knowledge into Genetic Algorithms. In: Davis L Ed. Genetic Algorithms and Simulated Ammealing, Pitman, 1987, 42～60
9. Orvosh D, Davis L. Using a Genetic Algorithm to Optimize Problems with Feasibility Constraints. In: Proc. of 1 st IEEE Conf. On Evolutionary Computation, 1994, 548～553
10. Chen H, Flann N S. Parallel Simulated Annealing and Genetic Algorithms: A Space of Hybrid Methods. In: Parallel Problem Solving
from Nature 3, Springer-Verlag, 1994, 428～438
11．李建军．超视距多机协同空战分析．西北工业大学硕士学位论文，2001
12．康立山等．非数值并行算法——模拟退火算法．北京：科学出版社，1994
13．王凌，郑大钟．基于Cauchy和Gaussian分布状态发生器的模拟退火算法．清华大学学报(自然科学版)，2000，40(9)：109-112
14. Dueck, G., Scheuer, T.. Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing[J]. J Comput Phys, 1990, 90: 161-175
15．程国采．战术导弹导引方法．北京：国防工业出版社，1996
16．李廷杰．导弹武器系统的效能及其分析．北京：国防工业出版社，2000
17．朱宝鎏，朱荣昌，熊笑非．作战飞机效能评估．北京：航空工业出版社，1993
18．李林森，佟明安．协同多目标攻击空战决策及其神经网络实现．航空学报，1999，20(4)：309-312
19．蓝伟华，赵春玲．空对空多机协同攻击多个目标的战术决策研究．火力与指挥控制，1999，24(1)：30-33
20．谢希权，李伟仁．单机多目标攻击逻辑的对策性决策．系统工程与电子技术，2000，22(7)：28-31
21．王红等．一种多目标攻击逻辑与决策方法．兵工学报，1997，18(3)：244-247
22．王宏伦，佟明安．多机空战仿真系统．西北工业大学学报，1998，16(1)：70-74
23．谭浩强．C程序设计．北京：清华大学出版社，1991
24．郑莉等．C++语言程序设计．北京：清华大学出版社，2000