Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.

翻译：过去十年间，无人驾驶飞行器（UAV）的发展显著加速，并被广泛应用于监视、危机管理或自动化任务规划等多个领域。最后一个领域涉及对包含多项任务、多架无人机及地面控制站的行动方案搜索，以及包括完工时间、燃油消耗或成本等在内的多个目标的优化。本研究采用一种多目标进化算法结合约束满足问题模型来解决该问题，其中约束满足模型被用于算法的适应度函数。该算法已在若干复杂度递增的任务中进行了测试，并对任务中不同要素的计算复杂度进行了分析。