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

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 58; no. 5; pp. 4396 - 4408
Main Authors: Wu, Guohua, Luo, Qizhang, Du, Xiao, Chen, Yingguo, Suganthan, Ponnuthurai Nagaratnam, Wang, Xinwei
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Ant colony optimization
Divide-and-conquer framework (DCF)
Earth Observing System
ensemble algorithm
exact algorithm
Heuristic methods
metaheuristic algorithm
observation satellite scheduling
Optimal scheduling
Orbits
Processor scheduling
Resource management
Satellite observation
Satellites
Scheduling
Tabu search
Task analysis
Task scheduling
ISSN:0018-9251, 1557-9603
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2022.3160993