Multi-Granular Genetic Algorithm Based Task Allocation for Heterogeneous UAVs

The unmanned aerial vehicles (UAVs) cooperative multi-task allocation systems have been widely used in recent years in areas such as land aerial photography, transportation of materials in disaster areas and so on. With the increase of the complex relationship between the mission and the UAV, it is difficult for genetic algorithm to find the global optimal solution in the search process. This paper proposes a new Multi-Granular Genetic Algorithm (MGGA) to solve the UAV cooperative multi-task allocation problem (CMTAP), accounting for multiple constraints in real scenarios. To enhance the quality of the initial population of genetic algorithm, a data preprocessing method via a typical clustering algorithm is proposed at first. Aiming to improve the solution speed of the standard genetic algorithm, MGGA incorporates the concept of Multi-Granular Cognition and combines granular-ball computing with the genetic algorithm to solve the CMTAP. Finally, through simulation experiments in different scenarios, the algorithm is proved to have better optimization ability and convergence effect.