用于武器目标分配的自适应并行遗传算法

doi:10.3901/JME.260400

摘要/Abstract

摘要： 战略要地防空武器目标分配是一个典型的多目标优化问题，其目的是生成合理有效的火力分配方案来指导防御行动。对此，建立了一个包含来袭敌方目标剩余威胁值和武器成本的双目标武器目标分配模型。为快速求解该模型以实现实时应用，提出了改进的自适应并行非支配排序遗传算法。首先，引入一种基于优势度矩阵的排序算法，以改进快速非支配排序并降低计算复杂度。其次，设计一种基于截断均值的自适应算子，用于交叉和变异操作，以提高算法的收敛性。然后，计算过程采用了自适应迁移策略进行并行化处理，以进一步减少求解时间。实验部分建立六种仿真场景验证了所提算法的有效性。

关键词: 武器目标分配, 多目标优化, 自适应算子, 并行计算, NSGA-III

Abstract: The weapon-target assignment (WTA) for the air defense of strategic point is a typical multi-objective optimization problem, of which the purpose is to generate a reasonable and effective firepower allocation scheme to guide the defensive operations. A WTA model containing a bi-objective function of the remaining threat value of the incoming enemy target and the weapon cost is built. To solve the model quickly for real-time application, the improved adaptive-parallel non-dominated sorting genetic algorithm is proposed. Firstly, a vector sorting algorithm is introduced to improve the fast non-dominated sorting and reduce the computational complexity. Secondly, a truncated mean-based adaptive law is designed for crossover and variational operations to improve the convergence of the algorithm. Thirdly, the optimization is parallelized with an adaptive migration strategy to further reduce the solving time. Six simulation scenarios are constructed in the experiments to validate the proposed algorithm.

Key words: weapon-target assignment, multi-objective optimization, adaptive operator, parallel computation, NSGA-III

中图分类号:

E917

刘富樯, 周伦, 刘中阳, 褚志刚, 蒲华燕, 罗均. 用于武器目标分配的自适应并行遗传算法[J]. 机械工程学报, 2026, 62(11): 116-131.

LIU Fuqiang, ZHOU Lun, LIU Zhongyang, CHU Zhigang, PU Huayan, LUO Jun. An Adaptive-parallel Genetic Algorithm Applying for Weapon-target Assignment[J]. Journal of Mechanical Engineering, 2026, 62(11): 116-131.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260400

http://www.cjmenet.com.cn/CN/Y2026/V62/I11/116

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