Evolutionary decision-makings for the dynamic weapon-target assignment problem

  • Jie Chen
  • Bin Xin
  • ZhiHong Peng
  • LiHua Dou
  • Juan Zhang


The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general “virtual” representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.


decision-making dynamic weapon-target assignment (DWTA) military command and control evolutionary computation memetic algorithms constraints handling 


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Copyright information

© Science in China Press and Springer Berlin Heidelberg 2009

Authors and Affiliations

  • Jie Chen
    • 1
    • 2
  • Bin Xin
    • 1
    • 2
  • ZhiHong Peng
    • 1
    • 2
  • LiHua Dou
    • 1
    • 2
  • Juan Zhang
    • 1
    • 2
  1. 1.School of AutomationBeijing Institute of TechnologyBeijingChina
  2. 2.Key Laboratory of Complex System Intelligent Control and DecisionMinistry of EducationBeijingChina

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