📄 中文摘要
研究提出了一种新的不确定敏捷地球观测卫星调度问题（UAEOSSP），与静态的敏捷地球观测卫星调度问题（AEOSSP）不同，该问题考虑了一系列不确定因素，如任务收益、资源消耗和任务可见性，以反映实际信息在事先是未知的现实。设计了一种有效的遗传编程超启发式算法（GPHH），用于自动生成调度策略。所进化的调度策略能够实时调整计划，并表现出色。实验结果表明，进化的调度策略显著优于精心设计的前瞻性启发式算法（LAHs）和手动设计的启发式算法。
一种有效的遗传编程超启发式算法用于不确定的敏捷卫星调度
📄 English Summary
An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling
The study introduces a novel problem known as the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP), which differs from the static Agile Earth Observation Satellite Scheduling Problem (AEOSSP) by incorporating a range of uncertain factors such as task profit, resource consumption, and task visibility. This reflects the reality that actual information is inherently unknown in advance. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can adjust plans in real-time and perform exceptionally well. Experimental results demonstrate that the evolved scheduling policies significantly outperform both well-designed Look-Ahead Heuristics (LAHs) and manually designed heuristics.