用遗传编程自动生成卫星调度策略，应对任务利润等不确定性。
An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling
- 用遗传编程演化实时调度策略，自动适应不确定环境。
- 相比传统启发式方法，平均提升5.03%至8.14%的调度性能。
- 适合需要动态响应的遥感卫星任务规划场景。
本文研究一种新问题——不确定敏捷地球观测卫星调度问题（UAEOSSP）。与静态的AEOSSP不同，该问题考虑任务收益、资源消耗和任务可见性等多种不确定性因素，更贴近实际中信息未知的现实情况。为此设计了一种高效的遗传编程超启发式（GPHH），可自动生成调度策略。所演化出的策略能实时调整计划，表现优异。实验表明，相比精心设计的前瞻启发式（LAHs）和人工设计的启发式（MDHs），GPHH生成的策略平均分别提升5.03%和8.14%。
原文摘要 · Abstract (English)
This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and task visibility) in order to reflect the reality that the actual information is inherently unknown beforehand. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can be utilized to adjust plans in real time and perform exceptionally well. Experimental results demonstrate that evolved scheduling policies significantly outperform both well-designed Look-Ahead Heuristics (LAHs) and Manually Designed Heuristics (MDHs). Specifically, the policies generated by GPHH achieve an average improvement of 5.03% compared to LAHs and 8.14% compared to MDHs.
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