基于SAGWO算法的UCAVs动态协同任务分配

通过分析无人作战飞机（UCAV）优势概率和任务联合威胁以及定义任务时间，建立了以目标价值毁伤、编队损耗代价和时间消耗为性能指标的多无人作战飞机（UCAVs）多约束动态任务分配数学模型，采用改进的灰狼优化（GWO）算法对数学模型进行求解；针对基本GWO算法求解早熟的缺点，给出了自适应调整策略和跳出局部最优策略，引入了二次曲线控制方法；对UCAVs动态协同任务分配特点，设计了目标任务序列编码方式，提出了基于自适应GWO（SAGWO）算法的UCAVs多目标动态任务分配方法。从静态与动态2种情况分别对该方法进行仿真验证；仿真结果表明，该方法是有效的，相比较于其他算法，其优化过程快速精准。

Dynamic UCAVs cooperative task allocation based on SAGWO algorithm

Through analyzing unmanned combat aerial vechicle (UCAV) advantage probability and task joint threat and defining task time, the task allocation model for UCAVs with multi-constraint dynamic task allocation is built up, which takes target value damage, UCAV attrition and task expending time as the performance indexes, and the improved grey wolf optimization (GWO) algorithm is used to solve the model. Aimed at the flaw of early convergence from the original algorithm, the GWO algorithm is improved by proposing a self-adaptive adjustment strategy and a step-out local optimum strategy, using quadratic curve control method. According to the characteristics of UCAVs dynamic cooperative task allocation, target task sequence coding is designed to present the UCAVs dynamic task allocation method based on self-adaptive GWO (SAGWO) algorithm. Finally, the simulation results for static and dynamic task allocation show that the task allocation method based on SAGWO algorithm is valid, and compared with other algorithms, the optimizing process is rapid and accurate.