This article presents a performance improvement of a bio-inspired quadrotor swarm for a flocking task with an unknown target zone. Because swarm behavior depends on repulsion, orientation and attraction tendencies between members, these parameters can be set properly for this improvement. A simulator of the swarm in the proposed task is made involving a dynamic modelling of a quadrotor and considering two scenarios, without and with obstacles. Task execution is evaluated by multiple objective functions which must be minimized making use of multi-objective optimization techniques. A comparison between Nondominated Sorting Genetic Algorithm II with Differential Evolution (NSGA-II-DE) and Multi-Objective Particle Swarm Optimization (MOPSO) is made. The results indicate relevance of the proper set of repulsion, orientation and attraction parameters in order to maintain the properties of the swarm like aggregation while executes a navigation-search task when scenario involves a different number of quadrotors or when there are obstacles in the arena.

本文介绍了针对目标区域未知的植绒任务，采用生物启发式四旋翼机群的性能改进。由于群体行为取决于成员之间的排斥，定向和吸引趋势，因此可以适当设置这些参数以进行改进。提出的任务中的群体仿真器包括四旋翼的动态建模，并考虑了两种情况，无障碍和有障碍。任务执行由多个目标函数评估，必须使用多目标优化技术将其最小化。比较了具有差异演化的非支配排序遗传算法II（NSGA-II-DE）和多目标粒子群优化算法（MOPSO）。结果表明适当的排斥力是相关的，