Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling
With the rise of low-cost launches, miniaturized space technology, and commercialization, the cost of space missions has dropped, leading to a surge in flexible Earth observation satellites. This increased demand for complex and diverse imaging products requires addressing multi-objective optimizati...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2458024 |
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| author | He Wang Weiquan Huang Sindri Magnússon Tony Lindgren Chen Chen Junyu Wu Yanjie Song |
| author_facet | He Wang Weiquan Huang Sindri Magnússon Tony Lindgren Chen Chen Junyu Wu Yanjie Song |
| author_sort | He Wang |
| collection | DOAJ |
| description | With the rise of low-cost launches, miniaturized space technology, and commercialization, the cost of space missions has dropped, leading to a surge in flexible Earth observation satellites. This increased demand for complex and diverse imaging products requires addressing multi-objective optimization in practice. To this end, we propose a multi-objective agile Earth observation satellite scheduling problem (MOAEOSSP) model and introduce a reinforcement learning-based multi-objective grey wolf optimization (RLMOGWO) algorithm. It aims to maximize observation efficiency while minimizing energy consumption. During population initialization, the algorithm uses chaos mapping and opposition-based learning to enhance diversity and global search, reducing the risk of local optima. It integrates Q-learning into an improved multi-objective grey wolf optimization framework, designing state-action combinations that balance exploration and exploitation. Dynamic parameter adjustments guide position updates, boosting adaptability across different optimization stages. Moreover, the algorithm introduces a reward mechanism based on the crowding distance and inverted generational distance (IGD) to maintain Pareto front diversity and distribution, ensuring a strong multi-objective optimization performance. The experimental results show that the algorithm excels at solving the MOAEOSSP, outperforming competing algorithms across several metrics and demonstrating its effectiveness for complex optimization problems. |
| format | Article |
| id | doaj-art-700fb57e75d640e89a61be0f1ce224a4 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-700fb57e75d640e89a61be0f1ce224a42025-02-03T03:08:34ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2025.2458024Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite schedulingHe Wang0Weiquan Huang1Sindri Magnússon2Tony Lindgren3Chen Chen4Junyu Wu5Yanjie Song6College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, People's Republic of ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, People's Republic of ChinaDepartment of Computer and Systems Sciences, Stockholm University, Stockholm, SwedenDepartment of Computer and Systems Sciences, Stockholm University, Stockholm, SwedenCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, People's Republic of ChinaCollege of Mechanical and Electrical Engineering, Harbin Institute of Technology, Harbin, People's Republic of ChinaNational Engineering Research Center of Maritime Navigation System, Dalian Maritime University, Dalian, People's Republic of ChinaWith the rise of low-cost launches, miniaturized space technology, and commercialization, the cost of space missions has dropped, leading to a surge in flexible Earth observation satellites. This increased demand for complex and diverse imaging products requires addressing multi-objective optimization in practice. To this end, we propose a multi-objective agile Earth observation satellite scheduling problem (MOAEOSSP) model and introduce a reinforcement learning-based multi-objective grey wolf optimization (RLMOGWO) algorithm. It aims to maximize observation efficiency while minimizing energy consumption. During population initialization, the algorithm uses chaos mapping and opposition-based learning to enhance diversity and global search, reducing the risk of local optima. It integrates Q-learning into an improved multi-objective grey wolf optimization framework, designing state-action combinations that balance exploration and exploitation. Dynamic parameter adjustments guide position updates, boosting adaptability across different optimization stages. Moreover, the algorithm introduces a reward mechanism based on the crowding distance and inverted generational distance (IGD) to maintain Pareto front diversity and distribution, ensuring a strong multi-objective optimization performance. The experimental results show that the algorithm excels at solving the MOAEOSSP, outperforming competing algorithms across several metrics and demonstrating its effectiveness for complex optimization problems.https://www.tandfonline.com/doi/10.1080/17538947.2025.2458024Earth observation satellitereinforcement learningQ-learningschedulingmulti-objective optimizationgrey wolf algorithm |
| spellingShingle | He Wang Weiquan Huang Sindri Magnússon Tony Lindgren Chen Chen Junyu Wu Yanjie Song Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling International Journal of Digital Earth Earth observation satellite reinforcement learning Q-learning scheduling multi-objective optimization grey wolf algorithm |
| title | Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling |
| title_full | Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling |
| title_fullStr | Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling |
| title_full_unstemmed | Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling |
| title_short | Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling |
| title_sort | crowding distance and igd driven grey wolf reinforcement learning approach for multi objective agile earth observation satellite scheduling |
| topic | Earth observation satellite reinforcement learning Q-learning scheduling multi-objective optimization grey wolf algorithm |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2458024 |
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