Agile Earth Observation Satellite Mission Planning Based on Improved Hybrid Genetic Algorithm

Abstract

The mission planning problem of the agile earth satellite observing multiple stationary point targets on the ground is essentially a complex NP-hard problem with multiple constraints. This paper analyzes the constraints faced by AEOs in the observation process, and constructs a satellite mission scheduling model based on target revenue and multiple constraints. An improved hybrid genetic algorithm is designed to solve the model. In order to improve the mutation process of the traditional genetic algorithm, the optimization idea of the tabu search algorithm is added, and the crossover and mutation operators introduce adaptive probability, which improves the probability of the algorithm searching for the global optimal solution and accelerates the convergence speed of the algorithm. Experiments are designed for the problem of regional dense target observation, and compared with various traditional genetic algorithms. The experimental results verify the effectiveness and convergence effect of the algorithm.