Open AccessOptimization design of shaped charge based on improved genetic algorithm
Author(s) -
Cheng Yong Tang,
Biao Xue,
Lixia Wang
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/4/042034
Subject(s) - crossover , genetic algorithm , algorithm , subspace topology , charge (physics) , meta optimization , mathematical optimization , computer science , process (computing) , scheme (mathematics) , operator (biology) , optimization problem , mathematics , artificial intelligence , physics , mathematical analysis , biochemistry , chemistry , quantum mechanics , repressor , transcription factor , gene , operating system
In order to improve the design efficiency of shaped charge, reduce the number of tests and shorten the development period, an improved genetic algorithm is introduced in the design process of shaped charge. Compared with the design scheme in the iterative process of traditional optimization design, the improved genetic algorithm introduces the multi parent crossover operator, randomly selects N individuals, forms the subspace, and hybridizes N individuals in the subspace to form a new individual. Because of the number of individuals involved in the hybridization, it is easier to calculate the optimal solution, and has stronger optimization ability. Taking a shaped charge as an example, the structural parameters of shaped charge are optimized by the improved genetic algorithm. After the optimization, it is verified by the vertical static armor breaking test of shaped charge, The results show that the optimization design method of shaped charge structure based on the improved genetic algorithm is reasonable and feasible, and the operational and technical indexes of the optimization scheme meet the requirements.