基于遗传蚁群算法的武器目标分配优化方法

Weapon Target Assignment Optimization Method Based on Genetic-Ant Colony Optimization

摘要:

针对演化算法求解武器目标分配问题求解精度不高的问题，提出结合蚁群优化的遗传蚁群算法。使用混沌映射随机初始化方法，通过逻辑混沌映射初始化种群，提升初始种群的多样性；通过多样性交换策略，交换染色体部分区域值，进一步提高算法的多样性；基于蚁群优化思想，提出蚁群重组准则，利用蚂蚁的搜索行为增强算法的收敛性。使用12个测试用例，进行消融实验证明了各策略的有效性，与7种典型方法进行对比实验，结果验证了所提方法的优越性。

Abstract:

In order to tackle the low accuracy issue of evolutionary algorithms in solving weapon target assignment problems, an algorithm with ant colony optimization named Genetic Algorithm based on Ant Colony Optimization is proposed. Chaotic mapping random initialization method is developed to initialize the population through logical chaotic mapping to improve the diversity of the initial population. A diversity swap strategy is put forward to swap some chromosome regions' values to further improve the algorithm’s diversity. Based on the idea of ant colony optimization, the ant colony recombination criterion is raised to enhance the algorithm’s convergence by using the ant’s search behavior. 12 test instances are used for experiments, the ablation experiment proves the effectiveness of each strategy, and the comparative experiment results with 7 typical methods verify the superiority of the proposed method.