Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms
Computers and Operations Research
Proceedings of the 38th conference on Winter simulation
Priority-based assignment and routing of a fleet of unmanned combat aerial vehicles
Computers and Operations Research
Convergence time analysis of particle swarm optimization based on particle interaction
Advances in Artificial Intelligence
| Hi-index | 0.00 |
An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.