The problem of task allocation in a multi-robot system is the situation where we have a set of tasks and a number of robots; then each task is assigned to the appropriate robots with the aim of optimizing some criteria subject to constraints, e.g., allocate the maximum number of tasks. We propose an effective solution to address this problem. It implements a two-stage methodology: first, a global allocation based of the well-known firefly algorithm, and then, a local allocation combining advantages of quantum genetic algorithms and artificial bee colony optimization. We compared our proposed solution to one solution from the state of the art. The simulation results show that our scheme significantly performs better than this solution. Our solution allocated \(100\%\) of the tasks (in every configuration tried in the experiments) and enhanced the allocation time by \(75\%\).

中文翻译：

---

FA–QABC–MRTA：解决多机器人任务分配问题的解决方案

在多机器人系统中，任务分配的问题是我们有一组任务和许多机器人。然后将每个任务分配给适当的机器人，以优化一些受约束条件的标准，例如分配最大数量的任务。我们提出了解决此问题的有效解决方案。它实现了两个阶段的方法：首先，基于著名的萤火虫算法进行全局分配，然后结合量子遗传算法和人工蜂群优化的优势进行局部分配。我们将提出的解决方案与现有技术中的一种解决方案进行了比较。仿真结果表明，我们的方案比该解决方案具有更好的性能。我们的解决方案分配了\（100 \％\）任务（在实验中尝试的每个配置中）并通过\（75 \％\）延长了分配时间。