A Review of Multi-Satellite Imaging Mission Planning Based on Surrogate Model Expensive Multi-Objective Evolutionary Algorithms: The Latest Developments and Future Trends

Multi-satellite imaging mission planning (MSIMP) is an important focus in the field of satellite application. MSIMP involves a variety of coupled constraints and optimization objectives, which often require extensive simulation and evaluation when solving, leading to high computational costs and slo...

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Veröffentlicht in:Aerospace Jg. 11; H. 10; S. 793
Hauptverfasser: Yang, Xueying, Hu, Min, Huang, Gang, Lin, Peng, Wang, Yijun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2024
Schlagworte:
adaptive strategy
Algorithms
Basic converters
Classification
Computational efficiency
Constraints
Efficiency
Environmental monitoring
Evolutionary algorithms
expensive multi-objective evolutionary algorithm
Flexibility
Genetic algorithms
Mathematical optimization
Methods
Mission planning
multi-satellite imaging mission planning
Multiple objective analysis
Optimization
Optimization algorithms
Pareto optimum
Quality assessment
Satellite imagery
Satellite imaging
Satellites
Scheduling (Management)
surrogate model
Trends
China
ISSN:2226-4310, 2226-4310
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Zusammenfassung:Multi-satellite imaging mission planning (MSIMP) is an important focus in the field of satellite application. MSIMP involves a variety of coupled constraints and optimization objectives, which often require extensive simulation and evaluation when solving, leading to high computational costs and slow response times for traditional algorithms. Surrogate model expensive multi-objective evolutionary algorithms (SM-EMOEAs), which are computationally efficient and converge quickly, are effective methods for the solution of MSIMP. However, the recent advances in this field have not been comprehensively summarized; therefore, this work provides a comprehensive overview of this subject. Firstly, the basic classification of MSIMP and its different fields of application are introduced, and the constraints of MSIMP are comprehensively analyzed. Secondly, the MSIMP problem is described to clarify the application scenarios of traditional optimization algorithms in MSIMP and their properties. Thirdly, the process of MSIMP and the classical expensive multi-objective evolutionary algorithms are reviewed to explore the surrogate model and the expensive multi-objective evolutionary algorithms based on MSIMP. Fourthly, improved SM-EMOEAs for MSIMP are analyzed in depth in terms of improved surrogate models, adaptive strategies, and diversity maintenance and quality assessment of the solutions. Finally, SM-EMOEAs and SM-EMOEA-based MSIMP are analyzed in terms of the existing literature, and future trends and directions are summarized.
Bibliographie:ObjectType-Article-1
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ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace11100793