Heterogeneous UAV fleet delivery route: a novel discrete sheep flock migrate optimization algorithm

Completing the delivery task of rescue supplies with the fastest delivery time during natural disasters is very important. For emergency logistics delivery scenarios in complex terrain environments, drone delivery has gradually become a hot issue. We introduce an optimization of heterogeneous UAV delivery path in complex task scenarios. The proposed model aims to minimize the total delivery time of multiple drones, taking into account not only the impact of different types of drones' endurance and load capacity on path selection, but also the timeliness of delivery tasks. To solve the proposed problem, we propose a novel discrete sheep flock migrate optimization (DSFMO) algorithm. As a type of swarm intelligence optimization algorithm, DSFMO algorithm has the characteristics of high efficiency, independent of mathematical models, and not easily trapped in local optima. In the case validation section, the case was designed based on the rescue scenario of the Sichuan earthquake, we analyzed and solved the problem model using the DSFMO algorithm, verified the effectiveness of the model and algorithm, and discussed different task scenarios and drone storage quantity. The experimental results indicate that, while ensuring the completion of disaster relief tasks and considering the load constraints of heterogeneous, the DSFMO algorithm can optimize the timeliness of drone delivery. This study can provide theoretical basis for drone logistics distribution in complex environments, and thus provide support and reference for drone path planning research.