Özalp, N.Ayan, U.Öztop, Erhan2016-02-112016-02-112015978-146737509-2http://hdl.handle.net/10679/2135https://doi.org/10.1109/ICAR.2015.7251517Due to copyright restrictions, the access to the full text of this article is only available via subscription.This research is focused on the cooperative multi-task assignment problem for heterogeneous UAVs, where a set of multiple tasks, each requiring a predetermined number of UAVs, have to be completed at specific locations. We modeled this as an optimization problem to minimize the number of uncompleted tasks while also minimizing total airtime and total distance traveled by all the UAVs. By taking into account the UAV flight capacities. For the solution of the problem, we adopted a multi-Traveling Salesman Problem (mTSP) method [1] and designed a new genetic structure for it so that it can be applied to cooperative multi-task assignment problems. Furthermore, we developed two domain specific mutation operators to improve the quality of the solutions in terms of number of uncompleted tasks, total airtime and total distance traveled by all the UAVs. The simulation experiments showed that these operators significantly improve the solution quality. Our main contributions are the application of the Multi Structure Genetic Algorithm (MSGA) to cooperative multi-task assignment problem and the development of two novel mutation operators to improve the solution of MSGA.engrestrictedAccessconferenceObject59960400038047100009310.1109/ICAR.2015.7251517Task assignmentGenetic algorithmUAVHeuristic task assignmentCooperative taskAgent2-s2.0-84957610016