Swarm-GAP is a heuristic that combines a swarm intelligence strategy with the generalized assignment problem (GAP) method. This approach is especially appropriate when there are agents engaged in a collaborative task, but in general, heuristics have drawbacks to optimize resource allocation. A previous work proposed the usage of three swarm-GAP variants to solve the task allocation problem among agents representing a group of Unmanned Aerial Vehicles (UAVs) aiming at the optimization of their resources usage applied in the context of static environments. However, there is a lack of empirical assessment of these algorithms in dynamic scenarios, i.e., with some attributes changing along the system execution. Such changes represent important features of real-world application scenarios, such as in military operations in which a number of non-expected events may happen, e.g., loss of members of the UAV-team or onboard sensor failure. Therefore, the contributions of this work are the performance evaluation of the original algorithms in dynamic context, and the extension of these algorithms to properly address more realistic dynamic scenarios. Considering changes in some attributes of the environment, a trade-off in terms of the quality in the mission performance and the overhead in the communication among the UAVs is explored. The empirical assessment of the original algorithms and the proposed extensions were performed by conducting independent replications in a scenario where the number of agents (UAVs) changes at runtime and adaptations occur autonomously to maintain the mission execution. The acquired results provide evidence that the proposed solution is capable of dealing with dynamic scenarios, covering the gap left by other works in the literature, and enriching the realism of applications in autonomous intelligent systems.

Swarm-GAP是一种结合了群体智能策略和广义分配问题（GAP）方法的启发式方法。当有代理商参与协作任务时，此方法特别合适，但总的来说，启发式方法在优化资源分配方面存在缺陷。先前的工作提出了三种swarm-GAP变体的用途，以解决代表一组无人机的特工之间的任务分配问题，旨在优化其在静态环境中的资源使用。然而，在动态场景中，即在某些属性随系统执行而变化的情况下，缺乏对这些算法的经验评估。此类更改代表了实际应用场景的重要功能，例如在军事行动中，可能会发生许多非预期事件，例如，无人机团队成员丢失或机载传感器故障。因此，这项工作的贡献在于在动态环境中对原始算法的性能评估，以及对这些算法的扩展，以正确应对更现实的动态场景。考虑到环境的某些属性的变化，在任务性能的质量和无人机之间的通信开销方面进行了权衡。对原始算法和建议的扩展的经验评估是通过在代理程序（UAV）数量在运行时发生更改并且自动进行适应以维护任务执行的情况下进行独立复制来执行的。