@article{LI2024, 
author = {Hai LI and Yongjun LI and Yuanhao LIU and Weihu ZHAO and Xin LI and Shanghong ZHAO},
title = {ESWO-based task-scheduling algorithm for agile earth observation satellites},
year = {2024},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {45},
number = {10},
pages = {329370},
keywords = {agile satellite, observation scheduling, time-dependent transition time, Evolutionary Squeaky Wheel Optimization (ESWO), adaptive update strategy},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2023.29370},
doi = {10.7527/S1000-6893.2023.29370},
abstract = {Agile Earth Observation Satellites (AEOSs) (abbreviated as agile satellite) with flexible attitude maneuvering capability greatly improve the Earth observation capability. However, the rapid increase in the number and length of visible time windows brings great challenges to agile satellite observation scheduling, and the specific time-dependent transition time of the agile satellites further complicates the observation scheduling problem. Therefore, the agile satellites observation scheduling problem has received extensive attention. To address the problem of observation scheduling of agile satellites, a mixed-integer nonlinear programming mathematical model aiming at maximizing observation profits is established with a consideration of the time-dependent transition time. Then, based on the framework of Evolutionary Squeaky Wheel Optimization (ESWO) algorithm with large neighborhood-oriented search capability, a heuristic agile satellites task-scheduling algorithm (ESASS) is proposed. In the proposed algorithm, five core operators of the ESWO algorithm are designed: analyzer, selection operation, mutation operation, priority sorter and constructor, according to the characteristics of AEOS observation scheduling problem. Then, an adaptive update strategy is designed based on the idea of simulated annealing algorithm to improve the solution speed and performance. A comparison of the proposed algorithm with the Adaptive Large Neighborhood Search (ALNS) algorithm, the classic heuristic insertion algorithm and the Genetic Algorithm (GA) -based observation scheduling algorithm verifies the effectiveness of the proposed algorithm. The experimental results show that the proposed ESASS algorithm can achieve greater benefits and number of observation tasks with shorter CPU runtime, and is applicable to the AEOSs observation scheduling problem with time-dependent transition time characteristics.}
}