A Multi-Objective Dynamic Mission-Scheduling Algorithm Considering Perturbations for Earth Observation Satellites

• Published in: Aerospace Vol. 11; no. 8; p. 643
• Main Authors: Li, Hai, Li, Yongjun, Liu, Yuanhao, Zhang, Kai, Li, Xin, Li, Yu, Zhao, Shanghong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Adaptive algorithms; Adaptive control; Algorithms; Deep learning; Design; dynamic mission-scheduling; earth observation satellite; Earth observations (from space); Energy consumption; Evolutionary algorithms; Genetic algorithms; Greedy algorithms; Mathematical models; multi-objective scheduling; Multiple objective analysis; Mutation; Perturbation; Profits; Real time; Satellite observation; Satellites; Scheduling; Search algorithms; SPEA2
• ISSN: 2226-4310; 2226-4310
• DOI: 10.3390/aerospace11080643

The number of real-time dynamic satellite observation missions has been rapidly increasing recently, while little attention has been paid to the dynamic mission-scheduling problem. It is crucial to reduce perturbations to the initial scheduling plan for the dynamic mission-scheduling as the perturbations have a significant impact on the stability of the Earth observation satellites (EOSs). In this paper, we focus on the EOS dynamic mission-scheduling problem, where the observation profit and perturbation are considered simultaneously. A multi-objective dynamic mission-scheduling mathematical model is first formulated. Then, we propose a multi-objective dynamic mission-scheduling algorithm (MODMSA) based on the improved Strength Pareto Evolutionary Algorithm (SPEA2). In the MODMSA, a novel two-stage individual representation, a minimum perturbation random initialization, multi-point crossover, and greedy mutation are designed to expand the search scope and improve the search efficiency. In addition, a profit-oriented local search algorithm is introduced into the SPEA2 to improve the convergence speed. Furthermore, an adaptive perturbation control strategy is adopted to improve the diversity of non−dominated solutions. Extensive experiments are conducted to evaluate the performance of the MODMSA. The simulation results show that the MODMSA outperforms other comparison algorithms in terms of solution quality and diversity, which demonstrates that the MODMSA is promising for practical EOS systems.