Improved differential optimization algorithm for multi-satellite joint earth observation scheduling

Abstract

Abstract The mission completion rate is an important evaluation criterion in the multi-satellite joint observation scheduling problem. This paper proposes a model to address this issue to maximize the mission completion rate. The task requirements and constraints were analyzed with the constellation’s ocean surveillance task as the background. According to the characteristics of the solution space, an improved differential teaching-learning-based optimization is proposed. The proposed algorithm employs a probability-based discretization method and a difference-based individual updating strategy. Simulation experiments demonstrate the effectiveness of the method by improving the task completion rate and convergence speed compared to the benchmark algorithm in the given test case.