王楠, 戴江斌. 基于INSGA-Ⅱ算法的武器−目标分配方法[J]. 云南大学学报(自然科学版), 2022, 44(6): 1155-1165. doi: 10.7540/j.ynu.20210647
引用本文: 王楠, 戴江斌. 基于INSGA-Ⅱ算法的武器−目标分配方法[J]. 云南大学学报(自然科学版), 2022, 44(6): 1155-1165. doi: 10.7540/j.ynu.20210647
WANG Nan, DAI Jiang-bin. Weapon-target assignment method based on INSGA-Ⅱ[J]. Journal of Yunnan University: Natural Sciences Edition, 2022, 44(6): 1155-1165. DOI: 10.7540/j.ynu.20210647
Citation: WANG Nan, DAI Jiang-bin. Weapon-target assignment method based on INSGA-Ⅱ[J]. Journal of Yunnan University: Natural Sciences Edition, 2022, 44(6): 1155-1165. DOI: 10.7540/j.ynu.20210647

基于INSGA-Ⅱ算法的武器−目标分配方法

Weapon-target assignment method based on INSGA-Ⅱ

  • 摘要: 武器−目标分配问题是经典的组合优化问题,对该问题的建模和优化开展研究具有重要意义. 针对间接对抗式条件下的静态武器−目标分配问题,提出基于INSGA-Ⅱ算法的武器−目标分配方法. 首先,分析了单武器平台载弹量约束、目标分配弹药量约束和总载弹量约束等约束条件,并建立了最大化目标打击收益和最小化打击弹药成本的问题模型;然后,在多目标优化与Pareto解集相关定义基础上,对INSGA-Ⅱ算法的关键方法机制进行设计,主要包括种群编码与解码方法、提高种群分布性方法、种群进化机制和约束处理机制;最后,设置小规模、中规模和大规模3种仿真场景,并验证了所提出算法在不同场景下的寻优能力.

     

    Abstract: The weapon-target assignment problem is a classic combinatorial optimization problem, and it is of great significance to carry out research on the modeling and optimization of this problem. Based on the analysis of existing research, a weapon-target assignment method based on the INSGA-Ⅱ algorithm is proposed. The constraints of the single weapon platform's ammunition capacity, the target's assigned ammunition capacity, and the total ammunition capacity constraint, are analyzed, and a problem model that maximizes the benefits of hitting targets and minimizes the cost of hitting ammunition is established. Based on the definition of multi-objective optimization and Pareto solution set, the key methods and mechanisms of the INSGA-Ⅱ algorithm are designed, which mainly include population encoding and decoding methods, methods to improve population distribution, population evolution mechanisms, and constraint handling mechanisms. Three simulation scenarios of small-scale, medium-scale and large-scale are set up, and the optimization ability of the proposed algorithm in different scenarios is verified.

     

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