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

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Abstract

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.

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Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Jie Chen, Bin Xin, ZhiHong Peng, LiHua Dou & Juan Zhang

  2. Key Laboratory of Complex System Intelligent Control and Decision, Ministry of Education, Beijing, 100081, China

    Jie Chen, Bin Xin, ZhiHong Peng, LiHua Dou & Juan Zhang

Authors
  1. Jie Chen
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  2. Bin Xin
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  3. ZhiHong Peng
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  4. LiHua Dou
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  5. Juan Zhang
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Corresponding author

Correspondence to Bin Xin.

Additional information

Supported by the National Natural Science Foundation of China (Grant No. 60374069), and the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)

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Chen, J., Xin, B., Peng, Z. et al. Evolutionary decision-makings for the dynamic weapon-target assignment problem. Sci. China Ser. F-Inf. Sci. 52, 2006–2018 (2009). https://doi.org/10.1007/s11432-009-0190-x

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  • DOI: https://doi.org/10.1007/s11432-009-0190-x

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