Multi-objective task optimization for UAV swarm based on improved NSGA-Ⅱ

UAV swarms are widely used in tasks such as personnel search and rescue, as well as military reconnaissance. In order to improve the efficiency of unmanned cluster in carrying out large-scale reconnaissance tasks, a multi-objective optimization model is constructed to minimize the flight time and maximize the detection revenue for the task allocation problem of UAV cluster with different sensors. By constructing integer task encoding and a population initialization method based on Voronoi partitioning, the quality of the initial solution is improved, and the genetic method in NSGA-II algorithm is restricted to shorten the optimization time. This algorithm can provide a set of non-dominated solutions, allowing for the selection of the shortest flight time or maximum profit plan based on preference. To cope with large-scale damage, an initial population is generated based on local task flow rules to achieve rapid task optimization. Simulation results show that compared to the original algorithm, the improved algorithm has significant advantages in task allocation and damage reconstruction of large-scale unmanned clusters.