基于 γ 随机搜索策略的无人机集群海上任务分配

In view of the characteristics of complex maritime combat situations, diverse combat missions, and heterogeneous combat units of unmanned aerial vehicle (UAV) clusters, a multi-objective mission assignment optimization model for maritime UAV clusters was established, and an improved discrete particle swarm optimization algorithm based on γ random search strategy (γ-DPSO) was proposed for this model. Firstly, the combat situation details and complex combat requirements were introduced into the mission assignment problem of UAV clusters, and a mission assignment combat model of UAV clusters that fitted the combat scenario was established. Secondly, based on the particle coding matrix, the equilibrium search strategy, the γ random search strategy, and the phased adaptive parameters were designed, and the improved discrete particle swarm optimization algorithm based on the γ random search strategy was proposed to solve the problem that the discrete particle swarm optimization algorithm was easy to fall into local optimum and caused immature convergence. The simulation results show that the proposed improved algorithm can effectively solve the multi-objective mission assignment problem of UAV clusters for the multi-objective mission assignment optimization model of UAV clusters established in this paper that meets the characteristics of maritime combat, and the proposed improved strategy improves the convergence speed and accuracy of the algorithm.