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Network-based structure optimization method of the anti-aircraft system

分类 一致性(知识库) 遗传算法 计算机科学 过程(计算) 集合(抽象数据类型) 帕累托原理 构造(python库) 多目标优化 生存能力 数学优化 约束(计算机辅助设计) 工程类 算法 人工智能 计算机网络 机器学习 数学 机械工程 程序设计语言 操作系统
作者
Qingsong Zhao,Junyi Ding,Jichao Li,Li Huachao,Boyuan Xia
出处
期刊:Chinese Journal of Systems Engineering and Electronics [Institute of Electrical and Electronics Engineers]
卷期号:34 (2): 374-395
标识
DOI:10.23919/jsee.2023.000019
摘要

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities. Firstly, the thought of combat network model (CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength (CAST) logic and influence network (IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network (TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed. Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-II (NSGA2) is used to solve the multi-objective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-III (NSGA3) and strength Pareto evolutionary algorithm-II (SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

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