基于粒子群算法的杀伤链多目标决策问题研究
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大连交通大学机械工程学院

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Research on Multi-Objective Decision-Making Problem of Kill Chain Based on Particle Swarm Optimization Algorithm
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    摘要:

    杀伤链是现代和未来作战中的关键要素,对于提高作战效率和维护国家安全具有重要意义。首先,针对杀伤网建模问题提出了一种基于作战系统整体架构和作战流程的资源节点杀伤网,在传统杀伤网模型的基础上参考杀伤链定义对打击作战环节进行流程细化分类,根据具体作战需求将辅助作战环节引入杀伤链;其次,设计了以毁伤性、时效性、经济性、抗毁性为目标函数的多目标杀伤链评估体系,采用改进粒子群算法对模型进行求解,对算法位置更新策略、外部存档的更新和融合排序策略等进行改进优化,通过多目标标准算例和模拟战场数据对所提模型及算法的有效性与实用性进行了验证。

    Abstract:

    The kill chain is a key element in modern and future warfare, which is of great significance for improving combat efficiency and maintaining national security. Firstly, in order to solve the problem of killing network modeling, a resource node killing network based on the overall architecture and operation process of the combat system was proposed, and on the basis of the traditional killing network model, the process of the strike operation link was refined and classified with reference to the definition of the kill chain, and the auxiliary operation link was introduced into the kill chain according to the specific combat requirements. Secondly, a multi-objective kill chain evaluation system with damage, timeliness, economy and indestructibility as the objective functions is designed, the improved particle swarm optimization is used to solve the model, the algorithm position update strategy, the update of external archives and the fusion ranking strategy are improved and optimized, and the effectiveness and practicability of the proposed model and algorithm are verified by multi-objective standard examples and simulated battlefield data.

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  • 收稿日期:2024-05-29
  • 最后修改日期:2024-05-30
  • 录用日期:2024-06-06
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