Qilin Lu, Yu Chen, Xiaogang Qi, and Lifang Liu
Heterogeneous, unmanned aerial vehicle, cooperative, multi-taskallocation, particle swarm optimisation algorithm
This paper proposes a discrete particle swarm optimisation (DPSO) algorithm for solving the heterogeneous unmanned aerial vehicle (UAV) task allocation problem. Such an algorithm takes task priority, resource constraints flight distance, and task revenue into account. First, the specific particle is designed according to the characteristics of the problem, and the corresponding relationship between the allocation plans and the particles is given. A modified strategy is presented for the infeasible particles. On this basis, the original particle swarm algorithm was transformed to a DPSO algorithm. Then, in order to improve the local search ability of particles, an elite operator is introduced on the basis of DPSO, and local search is initiated with a certain probability, forming a new search strategy (IDPSO). Simulation results show that DPSO can be reasonable in solving heterogeneous UAV multi-task problems when the problem size is small. The optimal solution obtained by the proposed IDPSO algorithm is better than the DPSO algorithm, and as the scale of the task allocation problem increases, the superiority of the IDPSO algorithm becomes more significant.
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