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

被引：0
|
作者：
Gong Z. [1 ]
Duan P. [1 ]
Liu Y. [1 ]
Chen C. [1 ]
Lü H. [1 ]
机构：
[1] Unit 63850 of PLA, Baicheng, 137001, Jilin
来源：
Binggong Xuebao/Acta Armamentarii | 2019年 / 40卷 / 07期
关键词：
Disposition optimization; Exterior ballistics networking; Fitness function; Genetic algorithm; Monte Carlo test; Penalty function;
D O I：
10.3969/j.issn.1000-1093.2019.07.021
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Board of Acta Armamentarii. All right reserved.
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页码：1503 / 1510
页数：7
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