Neighbor Search based Allocation Method for Satellite Swarm with Large Scale Tasks

With the increasing number of agile satellites, the number of on-orbit tasks is also increasing. The task planning for satellite swarm with large scale tasks plays an important role in the management of satellite swarm. Feasible solution finding in a large space composed by the mapping among satellites and tasks is an NP hard problem. Consequently, this paper adopts two-level large neighborhood search algorithm. Firstly, the planning for satellite swarm with large scale tasks is decomposed into a single satellite scheduling problem, and then based on the constraint guided search strategy, neighborhood search is carried out in the solution space. At the same time, in order to improve the search efficiency, a certain scale of population is designed and the PSO algorithm is used to accelerate the convergence. Finally, with the given parameters of satellites and tasks, the algorithm designed in this paper is verified by numerical simulation. The simulation results show that the method can distribute the observation tasks to each satellite evenly without conflict.