Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning
With the development of aerospace technologies, the mission planning of agile earth observation satellites has to consider several objectives simultaneously, such as profit, observation task number, image quality, resource balance, and observation timeliness. In this paper, a five-objective mixed-in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8418365/ |
| _version_ | 1818618747522383872 |
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| author | Longmei Li Hao Chen Jun Li Ning Jing Michael Emmerich |
| author_facet | Longmei Li Hao Chen Jun Li Ning Jing Michael Emmerich |
| author_sort | Longmei Li |
| collection | DOAJ |
| description | With the development of aerospace technologies, the mission planning of agile earth observation satellites has to consider several objectives simultaneously, such as profit, observation task number, image quality, resource balance, and observation timeliness. In this paper, a five-objective mixed-integer optimization problem is formulated for agile satellite mission planning. Preference-based multiobjective evolutionary algorithms, i.e., T-MOEA/D-TCH, T-MOEA/D-PBI, and T-NSGA-III are applied to solve the problem. Problem-specific coding and decoding approaches are proposed based on heuristic rules. Experiments have shown the advantage of integrating preferences in many-objective satellite mission planning. A comparative study is conducted with other state-of-the-art preference-based methods (T-NSGA-II, T-RVEA, and MOEA/D-c). Results have demonstrated that the proposed T-MOEA/D-TCH has the best performance with regard to IGD and elapsed runtime. An interactive framework is also proposed for the decision maker to adjust preferences during the search. We have exemplified that a more satisfactory solution could be gained through the interactive approach. |
| first_indexed | 2024-12-16T17:26:30Z |
| format | Article |
| id | doaj.art-ce25eaef624643358b707acfbf37d177 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:26:30Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ce25eaef624643358b707acfbf37d1772022-12-21T22:23:02ZengIEEEIEEE Access2169-35362018-01-016409634097810.1109/ACCESS.2018.28590288418365Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission PlanningLongmei Li0https://orcid.org/0000-0002-6347-6108Hao Chen1Jun Li2Ning Jing3Michael Emmerich4College of Electronic Science and Engineering, National University of Defense Technology, Changsha, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha, ChinaLeiden Institute of Advanced Computer Science, Leiden University, Leiden, The NetherlandsWith the development of aerospace technologies, the mission planning of agile earth observation satellites has to consider several objectives simultaneously, such as profit, observation task number, image quality, resource balance, and observation timeliness. In this paper, a five-objective mixed-integer optimization problem is formulated for agile satellite mission planning. Preference-based multiobjective evolutionary algorithms, i.e., T-MOEA/D-TCH, T-MOEA/D-PBI, and T-NSGA-III are applied to solve the problem. Problem-specific coding and decoding approaches are proposed based on heuristic rules. Experiments have shown the advantage of integrating preferences in many-objective satellite mission planning. A comparative study is conducted with other state-of-the-art preference-based methods (T-NSGA-II, T-RVEA, and MOEA/D-c). Results have demonstrated that the proposed T-MOEA/D-TCH has the best performance with regard to IGD and elapsed runtime. An interactive framework is also proposed for the decision maker to adjust preferences during the search. We have exemplified that a more satisfactory solution could be gained through the interactive approach.https://ieeexplore.ieee.org/document/8418365/Preferencesevolutionary many-objective optimizationEOS mission planningtarget regionMOEA/D |
| spellingShingle | Longmei Li Hao Chen Jun Li Ning Jing Michael Emmerich Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning IEEE Access Preferences evolutionary many-objective optimization EOS mission planning target region MOEA/D |
| title | Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning |
| title_full | Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning |
| title_fullStr | Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning |
| title_full_unstemmed | Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning |
| title_short | Preference-Based Evolutionary Many-Objective Optimization for Agile Satellite Mission Planning |
| title_sort | preference based evolutionary many objective optimization for agile satellite mission planning |
| topic | Preferences evolutionary many-objective optimization EOS mission planning target region MOEA/D |
| url | https://ieeexplore.ieee.org/document/8418365/ |
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