基于多种群协作混沌智能算法的舰载机出动调度

Takeoff scheduling of carrier plane based on multi-colonies cooperation and CLS intelligence algorithm

为了提高舰载机的出动效率,有必要对舰载机出动调度问题进行研究,为此提出了利用多种群协作混沌智能算法求解舰载机出动调度问题。首先对舰载机出动调度问题进行数学建模,将其转换为带有约束条件的多目标函数求最优解的问题;其次建立舰载机出动调度所需基础模型,以库兹涅佐夫号航母某一典型的出动任务为例,分别利用以融合多种群和混沌局部搜索后所改进的粒子群算法(HPSO)及普通粒子群算法(PSO)为核心的方法对该调度问题进行求解;最后进行了仿真实验,结果表明,HPSO算法在收敛性、平稳性及所求解结果质量等方面都优于PSO,其求解时间和所求解结果也基本满足实际使用的需要。因此,可以利用HPSO算法对舰载机出动调度问题进行求解。

In order to improve the takeoff efficiency of carrier plane, it was necessary to research the takeoff scheduling prob- lem of carrier plane. So this paper put forward one method which used hybrid particle swarm optimization（HPSO） to solve the takeoff scheduling problem of carrier plane. First, it made mathematic model of the problem by changing it into one optimiza- tion of multi-objective function with restriction problem. Second, it established some basic models. Then it chose one typical takeoff task of Kuznetsov as the example, and used the methods, which saw HPSO （ combing multi-colonies and chaotic local search） or PS0 as the center respectively, to solve the takeoff scheduling problem. In the end, it carried out simulation. The results show that the HPS0 method has advantages of astringency, smooth and higher accuracy comparing with PSO. And its calculation time and solution results meet the practical demand of the problem. So the HPS0 could be used to solve the takeoff scheduling problem of carrier plane.