基于遗传算法的Tiling覆盖策略天文卫星任务规划

天文卫星机遇目标任务规划是一个复杂的多目标优化问题。针对Tiling覆盖策略的机遇目标任务规划要求及其约束条件进行抽象，建立任务规划问题模型，在规划模型基础上设计基于遗传算法的多目标优化任务规划算法TPA，并通过实例数据验证了不同参数条件下的求解。在解决Tiling覆盖策略的天文卫星机遇目标多目标任务规划问题时，所提方法能够在保证算法收敛性的同时兼顾优先级和规划路径，满足规划需求。 

Mission Planning for Astronomical Satellite Based on Genetic Algorithm under Tiling Coverage Strategy

Astronomical observation is an important means for space scientific research. ToO (Target of Opportunity), such as GW (Gravitational Wave) and GRB (Gamma Ray Burst), are significant phenomena in astronomical observation. The planning of ToO observation is an important task. Astronomy satellite planning is a complex multi-objective optimization problem. In this paper, the mission planning requirements and constraints under tiling coverage strategy are abstracted, and the ToO planning model under tiling coverage strategy is established. Based on the model, a multi-objective optimization planning algorithm TPA (ToO Planning Algorithm) based on GA (Genetic Algorithm) is designed. An example is given to illustrate the solution under different parameters, where the simulation input data is provided by JAUBERT Jean of SVOM team. The simulation result shows that the TPA can effectively solve the multi-objective task planning problem of astronomical satellites ToO under coverage strategy.