An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling

作者：Yuning Chen, Junhua Xue, Wangqi Gu, Mingyan Shao · 发表于：2025 11th International Conference on Big Data and Information Analytics (BigDIA) · 年份：2025 · DOI：10.1109/bigdia68682.2025.11383438 · 被引用次数：2 · 研究领域：Computer Science

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.