1. School of Aeronautics and Astronautics， Zhejiang University， Hangzhou 310027， Zhejiang， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China
摘要针对多枚空空导弹攻击多架飞机的作战场景，研究集群协同的多目标任务分配，实现对多架飞机的拦截。针对战场环境高动态、高复杂性的特点，采取基于灰狼优化算法（grey wolf optimization， GWO）的实时任务分配方案。为解决灰狼优化算法容易陷入局部最优、易早熟的问题，提出了两方面的改进策略：一是优化种群，以佳点集理论来增强种群遍历性；二是改进算法，以禁忌搜索更新头狼位置来提高算法的全局搜索能力，使其更易跳出局部最优区域，并采用自适应调整策略来加快算法的收敛速度。最后，对两组由异构导弹组成的导弹群进行多目标任务分配的仿真实验，实验结果验证了算法的可行性和优越性，满足实时动态任务分配的要求。
Abstract：Aiming at the combat scenario of multiple air-to-air missiles attacking multiple aircrafts, the multi-objective task assignment of cluster collaboration is studied to intercept multiple aircrafts. In view of the highly dynamic and complex characteristics of the battlefield environment, a real-time task assignment scheme based on Grey Wolf Optimization (GWO) is adopted. In order to solve the shortcoming of the Grey Wolf Optimization, which is easy to fall into local optimum and easy to mature, two improvement strategies are proposed. One is to optimize the population, which uses the good point set theory to enhance the ergodicity of the population. The second is to improve the algorithm, using tabu search to update the position of the head to improve the global search ability of the algorithm, so that it can jump out of the local optimal region more easily, and adopt the adaptive adjustment strategy to speed up the convergence speed of the algorithm. Finally, the simulation experiment of multi-objective task assignment is carried out for a missile cluster consisting of two groups of heterogeneous missiles. The experimental results verify the feasibility and superiority of the algorithm, and meet the requirements of real-time dynamic task assignment.
