Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162516
Title: Ensemble of meta-heuristic and exact algorithm based on the divide and conquer framework for multi-satellite observation scheduling
Authors: Wu, Guohua
Luo, Qizhang
Du, Xiao
Chen, Yingguo
Suganthan, Ponnuthurai Nagaratnam
Wang, Xinwei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Wu, G., Luo, Q., Du, X., Chen, Y., Suganthan, P. N. & Wang, X. (2022). Ensemble of meta-heuristic and exact algorithm based on the divide and conquer framework for multi-satellite observation scheduling. IEEE Transactions On Aerospace and Electronic Systems, 58(5), 4396-4408. https://dx.doi.org/10.1109/TAES.2022.3160993
Journal: IEEE Transactions on Aerospace and Electronic Systems
Abstract: Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multi-satellite observation scheduling problem, this paper proposes an ensemble of meta-heuristic and exact algorithm 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 meta-heuristic 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 use an exact method (i.e., branch and bound, B&B) to solve this model. The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of EHE-DCF, we compare it with B&B, three divide-and-conquer based meta-heuristics, and a state-of-the-art meta-heuristic. Experimental results show that EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. EHE-DCF is especially efficient for large-scale satellite observation scheduling problems.
URI: https://hdl.handle.net/10356/162516
ISSN: 0018-9251
DOI: 10.1109/TAES.2022.3160993
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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