A fast scheduling method of multiple agile satellites based on neural network-guided genetic algorithm

Agile imaging satellites play important roles in gathering Earth surface information due to their expansive field-of- view, high-resolution observations, and flexibility in observation timing. Genetic algorithm (GA) has been one of the most commonly utilized meta-heuristic algorithms for solving multiple agile satellites (MAS) scheduling problems due to its stable and good performance. Nonetheless, the iterative convergence speed of GA is slow owing to the blindness of the preliminary search. To address this issue, this paper proposes an efficient meta-heuristic MAS scheduling method integrated with a neural network (NN). In the proposed method, an NN is applied to learn the historical scheduling experience and generate effective initial solutions, thereby improving the convergence speed of GA for MAS scheduling. Experimental results demonstrate that the proposed NN-guided GA (NN-GA) accelerates the convergence of GA by approximately 60% and exhibits higher efficiency for MAS scheduling compared to existing state-of-the-art methods.