基于遗传模糊聚类的机群编队最优分配方法

Optimized formation assignment for large-scale air fleet using fuzzy clustering and genetic algorithm

针对当前机群的编队分配存在效率低、编队分配结果不可靠、智能性差等问题,提出了一种新的结合遗传算法和模糊聚类算法的机群编队最优分配方法.该混合算法通过模糊聚类算法解决了机群的编队分配不确定性问题,并且通过对传统遗传操作算子的改进,采用改进的遗传算法有效地克服了模糊聚类算法容易陷入局部极小值和对初始条件敏感的缺点,使机群的编队分配能快速收敛至全局最优解.3组不同分布类型的机群编队分配算例结果表明,该混合算法具有较好的通用性、有效性和智能性,适用于机群的编队最优分配.

Aiming at the low efficiency, fallibility of formation assignment result and lack of intelligence in optimized formation assignment for large-scale air fleet, a new hybrid genetic fuzzy clustering algorithm （GFCA） was proposed for large-scale air fleet optimized formation assignment by incorporating the fuzzy clustering algorithm into the genetic algorithm （GA）. The GFCA solved the uncertainty problem of formation assignment for air fleet by fuzzy clustering algorithm, avoided the local minima and was robust to initialization by using improved GA, with new genetic arithmetic operators, so as to obtain the global optima for formation assignment quickly. The results of two examples show that the GFCA has better generalization, effectiveness and intelligence, and it is applicable to optimized formation assignment for large-scale air fleet.