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基于 AGE-MOEA 的杀伤链建模与优化方法

  • Beijing Institute of Technology
  • Jiangnan Institute of Mechatronic Design

科研成果: 期刊稿件文章同行评审

摘要

A modeling and optimization method of kill chains based on adaptive geometry estimation-based multi-objective evolutionary algorithm (AGE-MOEA) is proposed for the kill-chains design. Based on the idea of kill chain closure, the OODA cycle theory and the traditional weapon target assignment model, a multi-objective optimization mathematical model of kill chains design is constructed to realize the mathematical representation of kill chains design problem. The proposed model comprehensively considers three kinds of equipment, which are used for reconnaissance, command and attack, respectively, and takes the maximum attack efficiency, minimum weapon consumption and minimum damage threat as the objective functions, and takes the equipment use constraints, kill chain relation constraints and damage threshold constraints as constraint conditions. The algorithm flow of kill chains optimization design based on AGE-MOEA is proposed to solve the multi-objective optimization mathematical model of kill chains design. Through the numerical experiments and the kill chain design experiments in air defense and anti-missile combat scenarios, the results show that the proposed method can simultaneously pursue the optimization of attack efficiency, weapon consumption and damage threat on the premise of ensuring that all kill chains of each target are closed, and obtain a kill chain solution, which can form a distributed lethality. The scientificity of multi-objective optimization mathematical model of kill chains design, the effectiveness of AGE-MOEA algorithm in solving kill chains design problems and the feasibility of kill chain design method in practical military operations are verified.

投稿的翻译标题Modeling and Optimization Method of Kill Chains Based on AGE-MOEA
源语言繁体中文
页(从-至)2617-2628
页数12
期刊Binggong Xuebao/Acta Armamentarii
45
8
DOI
出版状态已出版 - 31 8月 2024

关键词

  • kill chain
  • kill web
  • multi-objective optimization
  • OODA loop

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