On Relaxing and Steady State Genetic Methods for Satellite Imaging Scheduling

Satellite imaging scheduling problem with energy and memory limit belongs to NP-hard problems. As the mathematical programming model is built, a Lagrangian relaxation method can provide tight upper bound, using a max-weighted path algorithm in the constraint graph and a sub-gradient optimizing method to explore the minimum upper bound. Then, permutation based stochastic local search algorithms are used to optimize imaging scheduling results. Finally, the testing results demonstrate the efficiency of these methods.