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The paper establishes a weapon target assignment problem mathematical optimization model based on maximizing strike efficiency and minimizing the cost of attacking multiple ground targets through cooperative strikes using multi loitering munitions after reconnaissance and effectiveness assessment. It proposes a dynamic population number genetic algorithm (GA) based on current evolutionary performance. Through simulation verification, the effectiveness of the algorithm is confirmed. Compared with genetic algorithms employing adaptive crossover and mutation probabilities, traditional genetic algorithms, and PSO algorithms, this approach is more capable of escaping local optima, effectively enhancing strike efficiency.

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Solving the Weapon Target Assignment Problem Based on Dynamic Population Genetic Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Haonan Shi,
  • Xueping Zhu

摘要

The paper establishes a weapon target assignment problem mathematical optimization model based on maximizing strike efficiency and minimizing the cost of attacking multiple ground targets through cooperative strikes using multi loitering munitions after reconnaissance and effectiveness assessment. It proposes a dynamic population number genetic algorithm (GA) based on current evolutionary performance. Through simulation verification, the effectiveness of the algorithm is confirmed. Compared with genetic algorithms employing adaptive crossover and mutation probabilities, traditional genetic algorithms, and PSO algorithms, this approach is more capable of escaping local optima, effectively enhancing strike efficiency.