带精英集并行遗传算法的无人机干扰资源调度

在中大规模无人机干扰资源调度中，针对现有模型约束条件简单、调度算法适用规模较小的问题，该文提出了带最少任务数约束的资源调度模型，以最大化干扰效益和最小化成本为目标，用层次分析法对效益与成本指标赋权，并设计了一种用精英集加快收敛的改进并行遗传算法。在中等规模和500:500(干扰资源数:目标数)的更大规模仿真实验中，所提算法与遗传算法、非支配排序遗传算法II、修复遗传算法、基于岛屿模型的并行遗传算法和自适应模拟退火遗传禁忌搜索算法的性能相比，能在更短的时长内达到较优的目标函数值。

Unmanned Aerial Vehicle Jamming Resource Scheduling Based on Parallel Genetic Algorithm with Elite Set

In order to solve the optimization problem of jamming resource scheduling in medium and large-scale Unmanned Aerial Vehicle (UAV) jamming scenarios, a jamming resource scheduling model that can meet the minimum number of tasks constraint is proposed to improve the simple constraints and small-scale solution algorithms of the existing models. The interference benefit and cost indicators are weighted by the analytic hierarchy process. Then an improved parallel genetic algorithm is designed, where the elite set is introduced to accelerate the convergence of the algorithm. The simulation results in medium scale and larger scale jamming situations, such as 500:500 (number of jamming resources: number of targets) show that the proposed algorithm converges faster and achieves better objective function value than the existing representative and improved genetic algorithms.