Improved whale optimization algorithm for increasing the task planning efficiency of agile earth observation satellites under a high target density
Target observation by agile Earth observation satellites (AEOSs) in environments with a high target density requires efficient resource utilization through continuous multitarget imaging during a single orbital revolution. However, traditional optimization algorithms fail to achieve convergence in t...
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| Published in: | Acta astronautica Vol. 238; pp. 1 - 11 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.01.2026
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| Subjects: | |
| ISSN: | 0094-5765 |
| Online Access: | Get full text |
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| Summary: | Target observation by agile Earth observation satellites (AEOSs) in environments with a high target density requires efficient resource utilization through continuous multitarget imaging during a single orbital revolution. However, traditional optimization algorithms fail to achieve convergence in task scheduling for AEOS constellations because of the complex solution space of this problem. Accordingly, this study developed an improved whale optimization algorithm to address the task scheduling problem for multiple AEOSs in environments with a high target density. A composite solution structure is first implemented to represent orbital revolutions and sequential target assignments for AEOS constellations, thus enabling the modeling of the complex relationships between targets and revolutions. A distance-controlled mechanism that regulates the sequence of revolutions of whales is then designed by mapping the order of revolutions to distances. This mechanism is integrated with a search strategy that involves dynamic balancing of global and local searches through parameter update rules. Moreover, an improved greedy search method is proposed for dynamically partitioning candidate targets, thereby substantially reducing the size of the solution space and improving efficiency. Simulation results revealed that the proposed algorithm exhibited high stability and considerably reduced satellite resource consumption in the examined cases compared with baseline methods. This algorithm can facilitate systematic task planning for large-scale AEOS constellations under complex constraints.
•Heuristic algorithm optimizes agile satellite cluster scheduling and maneuver sequences to reduce energy consumption.•Revolution-target structure optimized via adapted Whale Optimization Algorithm.•Redesigned WOA mechanisms map revolution-target to numerical planning.•Improved Greedy Search dynamically partitions targets for efficient search. |
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| ISSN: | 0094-5765 |
| DOI: | 10.1016/j.actaastro.2025.09.005 |