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 |
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MDPI AG
01.10.2024
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| ISSN: | 2226-4310, 2226-4310 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Hu, Min Huang, Gang Lin, Peng Yang, Xueying Wang, Yijun |
| Author_xml | – sequence: 1 givenname: Xueying orcidid: 0000-0001-7443-9110 surname: Yang fullname: Yang, Xueying – sequence: 2 givenname: Min surname: Hu fullname: Hu, Min – sequence: 3 givenname: Gang surname: Huang fullname: Huang, Gang – sequence: 4 givenname: Peng surname: Lin fullname: Lin, Peng – sequence: 5 givenname: Yijun surname: Wang fullname: Wang, Yijun |
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| CitedBy_id | crossref_primary_10_3390_aerospace12050420 |
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| SubjectTerms | 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 |
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