多巡飞弹协同攻击目标优化分配研究

摘要

作为多巡飞弹协同对地攻击任务的一项关键技术,任务分配是一个多维离散变量的优化求解问题.针对当前算法在优化问题求解中的求解速率与易实现性不太理想的问题,提出一种基于仿生物的群智能算法来对其进行求解.首先对多巡飞弹协同攻击问题进行分析,采用线性加权法构造分配优化模型,然后利用粒子群优化算法寻优速率快和易实现性的特点对分配优化模型进行求解,最后通过具体算例来验证模型的合理性和算法的优越性.数值仿真结果表明,粒子群优化算法可以比较容易地并且快速地寻找到优化模型的最优解,高效率地实现了多巡飞弹的协同攻击任务分配问题.%As a key technology of multi-loitering missile to ground attack missions,task allocation is an optimization problem with multidimensional discrete variables.Since problem-solving rate and practicability of the current algorithm used in optimization problem solving are not too ideal,a swarm intelligence algorithm based on bionic to solve this problem is put forward.First the problem of multi-loitering missile coordinated attack was analyzed,and the linear weighted method was used to construct an optimal allocation model.Then the particle swarm optimization algorithm with the characteristics of fast-searching and practicability was used for the optimal solution of allocation optimization mode.In the end,the specific examples were used to verify the rationality of the model and the superiority of algorithm.The results of numerical simulation table show that the particle swarm optimization algorithm can easily and quickly find the optimal solution of optimization model,and solve the allocation problem of multi-loitering missile coordinated attack missions efficiently.