Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 902-911.DOI: 10.11772/j.issn.1001-9081.2019071274

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Constrained multi-objective weapon-target assignment problem

ZHANG Kai, ZHOU Deyun, YANG Zhen, PAN Qian   

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi'an Shaanxi 710072, China
  • Received:2019-07-22 Revised:2019-10-14 Online:2020-03-10 Published:2019-11-20
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61602385, 61603299), the Fundamental Research Funds for the Central Universities (3102019ZX016).


张凯, 周德云, 杨振, 潘潜   

  1. 西北工业大学 电子信息学院, 西安 710072
  • 通讯作者: 张凯
  • 作者简介:张凯(1988-),男,安徽马鞍山人,博士研究生,主要研究方向:智能决策、先进航空火力控制;周德云(1964-),男,浙江义乌人,教授,博士,主要研究方向:航空火力与指挥控制、复杂系统建模与仿真;杨振(1993-),男,安徽滁州人,博士研究生,主要研究方向:武器系统建模、仿真与评估;潘潜(1988-),男,江苏镇江人,博士研究生,主要研究方向:复杂系统建模与仿真。
  • 基金资助:

Abstract: The traditional point-to-point saturation attack is not ideal choice facing high-density and multi-azimuth swarming intelligence targets. The maximum killing effect with weapon number less than target number can be achieved by selecting the appropriate types of weapons and the location of aiming points to realize the fire coverage. Considering the operational requirements of security targets, damage threshold and preference assignment, the Constrained Multi-objective Weapon-Target Assignment (CMWTA) mathematical model was established at first. Then, the calculation method of the constraint violation value was designed, and the individual coding, detection and repair as well as constraint domination were fused to deal with multiple constraints. Finally, the convergence metric for multi-objective weapon-target assignment model was designed, and the approaches were verified by the frameworks of Multi-Objective Evolutionary Algorithm (MOEA). In the comparison of three MOEA frameworks, the capacity of the Pareto sets of SPEA2 (Strength Pareto Evolutionary Algorithm Ⅱ) is mainly distributed in [21,25], that of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm Ⅱ) is mainly distributed in [16,20], and that of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition) is less than 16. In the verification of the repair algorithm, the algorithm makes the convergence metrics of three MOEA frameworks increased by 20 %, and the proportion of infeasible non-dominated solutions in Pareto solution set of 0%. The experimental results show that SPEA2 outperforms NSGA-Ⅱ and MOEA/D on distribution and convergence metric in solving CMWTA model, and the proposed repair algorithm improves the efficiency of solving feasible non-dominated solutions.

Key words: Weapon-Target Assignment (WTA), decision support, target grouping, multi-objective optimization, constraint handling

摘要: 面对未来作战中高密度、多方位的集群智能体,传统点对点饱和攻击已不是最佳策略,可通过选择合适的武器类型和作用点实现火力覆盖,达到武器数量小于目标数量的最大杀伤效果。综合考虑安全目标、毁伤门限、偏好指派等作战需求,首先,建立了多约束多目标武器-目标分配(CMWTA)数学模型;其次,设计了约束违反值的计算方法,并采用个体编码、检测修复和约束支配相结合的方式处理多约束;最后,设计了针对多目标武器-目标分配模型的收敛性度量指标,并基于多目标进化算法(MOEA)框架进行了仿真分析。其中在进化算法框架对比中,SPEA2下的Pareto集合容量主要分布于[21,25]区间内,NSGA-Ⅱ下的Pareto集合容量主要分布于[16,20],而MOEA/D下的Pareto集合容量均小于16;在修复算法验证中,修复算法将三种进化算法框架的Convergence指标提升了20%以上,且可将Pareto解集中不可行解的比例保持在0%。实验结果表明,在求解CMWTA模型中,SPEA2算法框架在分布性和收敛性上优于NSGA-Ⅱ和MOEA/D算法框架,且所提修复算法有效地提高了进化算法对非支配可行解的求解效率。

关键词: 武器-目标分配, 决策支持, 目标分群, 多目标优化, 约束处理

CLC Number: