Application of Multi-sample Genetic Algorithm in Weapon Exterior Ballistics Networking Test

doi:10.3969/j.issn.1000-1093.2019.07.021

Abstract

Abstract: For the networking measurement mode of exterior ballistic in weapon test, the optimal disposition of test equipment is studied to ensure the optimal ballistic measuring precision. Based on the point-by-point least squares solution model, the root-mean-square of total cumulative error of ballistic parameters is designed as precision evaluation criterion. Considering the nonlinear constraints of solution space in actual situation and focusing on the probability characteristics of genetic algorithm, a complete multi-sample genetic algorithm based on Monte Carlo test and population penalty function check is designed to reliably solve the problem of optimal disposition. The proposed genetic algorithm is used to simulate and evaluate an optimal disposition of test equipment. Monte Carlo experiments were made for 500 times, a globally optimal solution was obtained by statistically analyzing the experimental results, and the proposed algorithm was compared with the traditional method. The simulated results show that the proposed algorithm can be used to reduce the influence of probability characteristic of genetic algorithm, lock the global optimal solution with large probability events, and ensure the validity and reliability of the optimal solution. Key

Key words: exteriorballisticsnetworking, geneticalgorithm, dispositionoptimization, fitnessfunction, penaltyfunction, MonteCarlotest

CLC Number:

TJ06

GONG Zhihua， DUAN Pengwei， LIU Yang， CHEN Chunjiang， L Haidong. Application of Multi-sample Genetic Algorithm in Weapon Exterior Ballistics Networking Test[J]. Acta Armamentarii, 2019, 40(7): 1503-1510.

References

［1］刘利生, 郭军海, 刘元, 等. 空间轨迹测量融合处理与精度分析[M]. 北京:清华大学出版社, 2014.
LIU L S, GUO J H, LIU Y, et al. Fusion processing and accuracy analysis of space trajectory measurement[M]. Beijing:Tsinghua University Press, 2014. (in Chinese)
[2］周雪刚. 非凸优化问题的全局优化算法[D]. 长沙:中南大学, 2010.
ZHOU X G.Global optimization algorithm for nonconvex optimization problems[D]. Changsha:Central South University,2010. (in Chinese)
[3］汤可宗. 遗传算法与粒子群优化算法的改进及应用研究[D]. 南京:南京理工大学, 2011.
TANG K Z. Research on improvement and application of genetic algorithm and particle swarm optimization algorithm[D]. Nanjing:Nanjing University of Science and Technology, 2011. (in Chinese)
[4］ GONG D W, GUO G S, LU L, et al. Adaptive interactive genetic algorithms with individual interval fitness[J]. Progress in Natural Science, 2008,18(3):359-365.
[5］刘帅, 秦姗姗. 基于自适应遗传算法的空管雷达部署优化[J]. 通信技术, 2018, 51(4):870-874.
LIU S, QIN S S. Deployment optimization for air traffic radar based on AGA[J]. Communications Technology, 2018, 51(4):870-874. (in Chinese)
[6］韩聪聪, 何华锋, 杨宗浩. 基于单纯形-遗传算法的导弹主动段优化设计[J]. 电光与控制, 2017, 24(10):22-26.
HAN C C, HE H F, YANG Z H. Optimization design of missile active phase based on simplex method and GA[J]. Electronics Optics & Control, 2017, 24(10):22-26．(in Chinese)
[7］张浩为, 谢军伟, 张昭建, 等. 基于混合自适应遗传算法的相控阵雷达任务调度[J]. 兵工学报, 2017, 38(9):1761-1770.
ZHANG H W, XIE J W, ZHANG Z J, et al. Task scheduling of phased array radar based on hybrid adaptive genetic algorithm[J]. Acta Armamentarii, 2017, 38(9):1761-1770. (in Chinese)
[8］ WANG Y X, QIAN L J, GUO Z, et al. Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm[J].Journal of Systems Engineering and Electronics,2008,19(5):939-944.
[9］高晓光, 赵欢欢, 任佳. 基于蚁群优化的贝叶斯网络学习[J].系统工程与电子技术,2010,32(7):1509-1512.
GAO X G, ZHAO H H, REN J. Bayesian network learning on algorithm based on ant colony optimization [J]. Systems Engineering and Electronics, 2010,32(7): 1509-1512. (in Chinese)

[10］ BENENTI G, CASATI G, STRINI G. Principles of quantum computation and information[M]. Singapore: World Scientific Publishing Co. Pte. Ltd., 2007.
[11］ SAITOH A, RAHIMI R, NAKAHARA M.A quantum genetic algorithm with quantum crossover and mutation operations[J].Quantum Information Processing, 2014,13(3):737-755.
[12］ JIA P P, ZENG Y Y, FENG Y X. Best viewpoint selection for 3D visualization using particle swarm optimization[J]. Journal of System Simulation,2017,29(10):2384-2390.
[13］齐建军, 郭波, 张涛, 等. 基于改进粒子群算法的地空导弹使用保障设备优化配置[J].国防科技大学学报, 2014, 36(1): 178-183.
QI J J, GUO B, ZHANG T, et al. Configuring and optimizing the operational support equipment of the antimissile guided missile based on the improved PSO[J]. Journal of National University of Defense Technology, 2014, 36(1):178-183. (in Chinese)
[14］ CAI Z Q, SI S B, SUN S D, et al. Learning Bayesian network structure with immune algorithm[J].Journal of Systems Engineering and Electronics, 2015, 26(2):282-291.
[15］阮旻智, 李庆民, 刘天华. 编队防控火力分配建模及其优化方法研究[J]. 兵工学报,2010, 31(11):1525-1529.
RUAN M Z, LI Q M, LIU T H. Modeling and optimization on fleet antiaircraft firepower allocation[J]. Acta Armamentarii, 2010, 31(11):1525-1529. (in Chinese)
[16］ MIN H F, DUAN N. Neural network-based adaptive state-feedback control for high-order stochastic nonlinear systems[J]. Acta Automatica Sinica, 2014, 40(12):2968-2972.
[17］郑玉军, 田康生, 张金林, 等. 基于模糊神经网略的相控阵雷达任务调度[J].兵工学报,2016, 37(11):2010-2014.
ZHENG Y J, TIAN K S, ZHANG J L, et al. Task scheduling design of phased array radar based on fuzzy neural network [J]. Acta Armamentarii, 2016, 37(11):2010-2014. (in Chinese)
[18］ LI M W,KANG H G, ZHOU P F, et al. Hybrid optimization algorithm based on chaos,cloud and particle swarm optimization algorithm[J].Journal of Systems Engineering and Electronics, 2013, 24(2):324-334.
[19］ LI Y Q, WANG R X, XU M Q. Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm[J].Chinese Journal of Aeronautics, 2014, 27(3):678-687.
[20］赵华, 郭军海, 刘利生, 等.遗传算法在数据融合中的应用[J].飞行器测控学报,2011, 30(4):56-59.
ZHAO H, GUO J H, LIU L S, et al. Application of genetic algorithms in trajectory data fusion[J]. Journal of Spacecraft TT&C Technology, 2011, 30(4):56-59. (in Chinese)
[21］姜涛, 刘勇, 王海峰.基于遗传算法的多台光电经纬仪优化布站研究[J].计算机仿真,2009,26(5):28-31.
JIANG T,LIU Y,WANG H F. Optimization of the disposition of opto-electronic theodolite stations based on genetic algorithm [J]. Computer Simulation, 2009, 26(5):28-31. (in Chinese)
[22］刘吉英, 朱炬波, 刘也,等. 基于测速定轨体制的布站几何优化设计[J].宇航学报,2007, 28(1):53-57.
LIU J Y,ZHU J B, LIU Y, et al. Optimization of the distribution geometry of stations based on the system of orbit determining by use of rate data [J]. Journal of Astronautics, 2007, 28(1):53-57. (in Chinese)
[23］陈中起, 于雷, 鲁艺, 等. 对抗雷达网最优电子战布阵研究[J].兵工学报,2012, 33(1):89-94.
CHEN Z Q, YU L, LU Y, et al. Research on optimized electronic warfare embattling countermining radar net[J]. Acta Armamentarii, 2012, 33(1):89-94. (in Chinese)
[24］周明，孙树栋. 遗传算法原理及应用[M]. 北京：国防工业出版社，1999.
ZHOU M，SUN S D. Theory and applications of genetic algorithms [M]. Beijing：National Defense Industry Press，1999. (in Chinese)

第40卷
第7期2019 年7月兵工学报ACTA
ARMAMENTARIIVol.40No.7Jul.2019

[1]	SU Sheng, GU Sen, SONG Zhiqiang, LIU Ping. Military Cockpit Color Design Method Based on Deep Representation Learning and Genetic Algorithm [J]. Acta Armamentarii, 2024, 45(4): 1060-1069.
[2]	MA Ye， FAN Wenhui， CHANG Tianqing. Optimization Method of Unmanned Swarm Defensive Combat Scheme Based on Intelligent Algorithm [J]. Acta Armamentarii, 2022, 43(6): 1415-1425.
[3]	WANG Hexiang， MAO Xiaodong， PANG Liping， FENG Xiaohan. Design on Layout Optimization of Air Supply System for Special Vehicle Cabin Based on Multi-objective Genetic Algorithm [J]. Acta Armamentarii, 2022, 43(12): 2981-2988.
[4]	ZHAO Pengcheng, SONG Baowei, MAO Zhaoyong, DING Wenjun. Path Planning for UUV Underwater Recovery based on Improved Composite Adaptive Genetic Algorithm [J]. Acta Armamentarii, 2022, 43(10): 2598-2608.
[5]	TANG Runzhi, BAN Liming, QIAN Le, LEI Zhengjun, SONG Weixing. Optimization of Vehicle Maintenance Process Scheduling under Limited Resources [J]. Acta Armamentarii, 2021, 42(9): 2032-2039.
[6]	SI Jiyue， PANG Zhaojun， YOU Meng， FENG Guangbin， DU Zhonghua. Structural Design and Optimization of Space Tether-net [J]. Acta Armamentarii, 2021, 42(5): 1065-1073.
[7]	FENG Kuikui， ZHANG Faping， WANG Wuhong， ZHANG Wenjie， ZHANG Tianhui. Assembly Process Parameters Optimization of Turbine Engine Rotor System Based on Non-dominated Sorting Genetic Algorithm [J]. Acta Armamentarii, 2021, 42(5): 1092-1100.
[8]	GAO Ziyin， DU Minggang， LI Shenlong， LI Jin. Design of Shifting Rules of Automatic Transmission Based on Genetic Algorithm Optimization and Fuzzy Control DynamicOptimization [J]. Acta Armamentarii, 2021, 42(4): 684-696.
[9]	WANG Shaohua， L Huiqiang， DONG Yuansheng， ZHANG Yuan. Optimal Assignment of Accompanying Rush-repair Tasks of Equipment [J]. Acta Armamentarii, 2021, 42(1): 192-198.
[10]	LIU Qiu, SUN Jinwei, ZHANG Hua, HU Xu, GU Liang. Road Identification and Semi-active Suspension Control Based on Convolutional Neural Network [J]. Acta Armamentarii, 2020, 41(8): 1483-1493.
[11]	YE Haichao, FAN Wulin, QIN Guohua, LIN Feng, ZHENG Xu, YANG Yang. Optimization and Experimental Validation of Aluminum Alloy Plate Hot-rolling Based on Constraint of Warpage Threshold [J]. Acta Armamentarii, 2020, 41(8): 1623-1632.
[12]	YANG Guanjinzi， LI Jianchen， HUANG Hai， GUO Linna. Non-turntable Field-calibration Method for Gyroscope Based on Optimal Analysis [J]. Acta Armamentarii, 2020, 41(3): 577-584.
[13]	ZHAO Xingyu, BAI Chunhua, YAO Jian, SUN Binfeng. Parameters Calculation of JWL EOS of FAE Detonation Products [J]. Acta Armamentarii, 2020, 41(10): 1921-1929.
[14]	ZHANG Fuyi, WU Qin, ZHAO Xiaoyang, LIU Ying, WANG Guoyu. Multi-objective Optimization of Inlet Duct of Water-jet Propulsion Based on Response Surface Method [J]. Acta Armamentarii, 2020, 41(10): 2071-2080.
[15]	LIU Hao，ZHANG Ce，DING Wentao. Joint Fire Attack Mission Planning Method Based on Intelligent Confrontation Evolution [J]. Acta Armamentarii, 2019, 40(6): 1287-1296.