The Multi-Robot Task Allocation problem of is the situation where we have a set of tasks and a number of robots, and each task have to be assigned to the appropriate robots that optimize some criteria, e.g. allocate the maximum number of tasks. We present an effective solution to address this problem. It implements a two-stage methodology: first, a global allocation using firefly algorithm, then a local allocation combining artificial bee colony optimization and quantum genetic algorithms. Experimental results show the effectiveness of the proposed solution in terms of the number of allocated tasks and the allocation time.




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