基于NSGA-Ⅲ算法的集群目标来袭火力分配建模与优化

doi:10.3969/j.issn.1000-1093.2021.08.022

摘要/Abstract

摘要： 火力分配建模与优化作为集群目标来袭防御任务规划的关键环节，对提高防御效果、保证任务完成质量具有重要意义。针对集群目标来袭防御策略呈现出由传统点对点饱和攻击向合理火力覆盖转变的基本趋势，建立以攻击效益最大、自身剩余价值最大、武器消耗最小为目标函数，以毁伤门限、武器资源总数和0-1整数约束为约束条件的集群目标火力分配模型；提出基于非支配排序遗传算法-Ⅲ（NSGA-Ⅲ）的集群目标来袭火力分配优化框架，给出具体的优化流程；面向想定的作战任务进行仿真实现，并通过收敛性指标和间距指标对NSGA-Ⅲ算法与第2代强度Pareto进化算法、NSGA-Ⅱ算法的性能进行对比分析。结果表明，NSGA-Ⅲ算法各项性能更优，能够更有效地解决集群目标来袭火力分配建模与优化问题。

关键词: 集群目标, 火力分配, 多目标进化算法, NSGA-Ⅲ

Abstract: As an important part of the task planning of group targets defense, the modeling and optimization of weapon-target assignment are of great significance to improve the defense effect and ensure the quality of task completion. In view of a basic trend that the defense strategy changes from the traditional point-to-point saturation attack to a reasonable firepower coverage, a weapon-group targets assignment model is established by considering the effects of damage threshold, total weapon resources and 0-1 integer constraints. The proposed model is based on the principles of maximum attack effectiveness, its own maximum surplus value and minimum weapon consumption. The optimization framework of weapon-group targets assignment based on NSGA-Ⅲ is proposed, and the specific optimization process of the algorithm is given. The simulation of combat mission is realized, and the convergence metric and spacing metric are used to compare the performances of NSGA-Ⅲ, SPEA2 and NSGA-Ⅱ. The simulated results demonstrate that NSGA-Ⅲ has better performance, which can effectively solve the weapon-target assignment modeling and optimization problem of group targets defense.

Key words: grouptargets, weapon-targetassignment, multi-objectiveevolutionaryalgorithm, NSGA-Ⅲ

中图分类号:

E917

聂俊峰, 陈行军, 苏琦. 基于NSGA-Ⅲ算法的集群目标来袭火力分配建模与优化[J]. 兵工学报, 2021, 42(8): 1771-1779.

NIE Junfeng, CHEN Xingjun, SU Qi. Modeling and Optimization of Weapon-target Assignment for Group Targets Defense Based on NSGA-Ⅲ Algorithm[J]. Acta Armamentarii, 2021, 42(8): 1771-1779.

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