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

TL;DR

This paper introduces a novel uncertain satellite scheduling problem and proposes a genetic programming hyper-heuristic that automatically generates adaptive scheduling policies, significantly outperforming traditional heuristics in real-time Earth observation tasks.

Contribution

The paper presents a new problem formulation for uncertain satellite scheduling and develops a GPHH method that outperforms existing heuristics in dynamic, uncertain environments.

Findings

GPHH-generated policies outperform traditional heuristics by over 5% and 8%.

The approach effectively handles uncertainty in satellite scheduling.

Experimental results validate the superiority of the proposed method.

Abstract

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.