Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive
Airspace sectorization is a powerful means to balance the increasing air traffic flow and limited airspace resources, which is related to the efficiency and safety of operations. In order to divide sectors reasonably, a multi-objective optimization framework for 3D airspace sectorization is proposed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/3/216 |
| _version_ | 1797614171343290368 |
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| author | Weining Zhang Minghua Hu Jianan Yin Haobin Li Jinghan Du |
| author_facet | Weining Zhang Minghua Hu Jianan Yin Haobin Li Jinghan Du |
| author_sort | Weining Zhang |
| collection | DOAJ |
| description | Airspace sectorization is a powerful means to balance the increasing air traffic flow and limited airspace resources, which is related to the efficiency and safety of operations. In order to divide sectors reasonably, a multi-objective optimization framework for 3D airspace sectorization is proposed in this paper, including four core modules: Flight clustering, sector generation, workload evaluation, and sector optimization. Specifically, it clusters flights and generates initial sectors using a Voronoi diagram. To further optimize sector shape, the concept of dynamic density is introduced to evaluate the controller workload, based on which a sector optimization model is constructed. The model not only considers intra-sector and inter-sector workloads as objective functions but also sets hard constraints to meet operation and safety requirements. To solve it, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with prior knowledge and an external archive is designed. By analyzing the optimization results of actual operational data in the Singapore regional airspace, our approach obtains diverse optimal sectorization schemes for decision makers to choose from. Qualitative and quantitative experimental results confirm that the initial population strategy with prior knowledge significantly accelerates the convergence process. At the same time, the mechanism of the external archive effectively enriches the diversity of solutions. |
| first_indexed | 2024-03-11T07:06:00Z |
| format | Article |
| id | doaj.art-d8ba27ef6385452b8d381cc4fea36d78 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T07:06:00Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj.art-d8ba27ef6385452b8d381cc4fea36d782023-11-17T08:57:58ZengMDPI AGAerospace2226-43102023-02-0110321610.3390/aerospace10030216Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External ArchiveWeining Zhang0Minghua Hu1Jianan Yin2Haobin Li3Jinghan Du4College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaDepartment of Industrial Systems Engineering and Management, National University of Singapore, Singapore 119077, SingaporeCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaAirspace sectorization is a powerful means to balance the increasing air traffic flow and limited airspace resources, which is related to the efficiency and safety of operations. In order to divide sectors reasonably, a multi-objective optimization framework for 3D airspace sectorization is proposed in this paper, including four core modules: Flight clustering, sector generation, workload evaluation, and sector optimization. Specifically, it clusters flights and generates initial sectors using a Voronoi diagram. To further optimize sector shape, the concept of dynamic density is introduced to evaluate the controller workload, based on which a sector optimization model is constructed. The model not only considers intra-sector and inter-sector workloads as objective functions but also sets hard constraints to meet operation and safety requirements. To solve it, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with prior knowledge and an external archive is designed. By analyzing the optimization results of actual operational data in the Singapore regional airspace, our approach obtains diverse optimal sectorization schemes for decision makers to choose from. Qualitative and quantitative experimental results confirm that the initial population strategy with prior knowledge significantly accelerates the convergence process. At the same time, the mechanism of the external archive effectively enriches the diversity of solutions.https://www.mdpi.com/2226-4310/10/3/2163D airspace sectorizationmulti-objective optimizationgenetic algorithmprior knowledgeexternal archive |
| spellingShingle | Weining Zhang Minghua Hu Jianan Yin Haobin Li Jinghan Du Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive Aerospace 3D airspace sectorization multi-objective optimization genetic algorithm prior knowledge external archive |
| title | Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive |
| title_full | Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive |
| title_fullStr | Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive |
| title_full_unstemmed | Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive |
| title_short | Multi-Objective 3D Airspace Sectorization Problem Using NSGA-II with Prior Knowledge and External Archive |
| title_sort | multi objective 3d airspace sectorization problem using nsga ii with prior knowledge and external archive |
| topic | 3D airspace sectorization multi-objective optimization genetic algorithm prior knowledge external archive |
| url | https://www.mdpi.com/2226-4310/10/3/216 |
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