Ensemble of Metaheuristic and Exact Algorithm Based on the Divide-And-Conquer Framework for Multisatellite Observation Scheduling

Guohua Wu, Qizhang Luo, Xiao Du, Yingguo Chen, Ponnuthurai Nagaratnam Suganthan, Xinwei Wang*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)


Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multisatellite observation scheduling problem, this article proposes an ensemble of metaheuristic and exact algorithms based on a divide-And-conquer framework (EHE-DCF), including a task allocation phase and a task scheduling phase. In the task allocation phase, each task is allocated to a proper orbit based on a metaheuristic incorporated with a probabilistic selection and a tabu mechanism derived from ant colony optimization and tabu search, respectively. In the task scheduling phase, we construct a task scheduling model for every single orbit and solve the model by using an exact method (i.e., branch and bound, B&B). The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of the EHE-DCF, we compare it with B&B, three divide-And-conquer-based metaheuristics, and a state-of-The-Art metaheuristic. Experimental results show that the EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. The EHE-DCF is especially efficient for large-scale satellite observation scheduling problems.

Original languageEnglish
Pages (from-to)4396-4408
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number5
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
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  • Divide-And-conquer framework (DCF)
  • Ensemble algorithm
  • Exact algorithm
  • Metaheuristic algorithm
  • Observation satellite scheduling


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