Hybrid Approach for Airspace Sectorization Based on K-Means Clustering and Genetic Algorithm Optimization
This paper presents a genetic optimization approach for airspace sectorization that combines K-means clustering and a genetic algorithm (GA). The algorithm minimizes the operating costs related to airspace complexities, unbalanced workloads, and inter-sector coordination requirements while respectin...
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| Veröffentlicht in: | 2024 International Conference of the African Federation of Operational Research Societies (AFROS) S. 1 - 5 |
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| Sprache: | Englisch |
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03.11.2024
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| Abstract | This paper presents a genetic optimization approach for airspace sectorization that combines K-means clustering and a genetic algorithm (GA). The algorithm minimizes the operating costs related to airspace complexities, unbalanced workloads, and inter-sector coordination requirements while respecting geometric constraints such as minimum distance, convexity, and connectivity. The algorithm initiates sectorization solutions using K-means clustering and then iteratively refines them using GA operations. A comparative analysis was conducted on ten tests, assigning various weights to cost components. The hybrid approach was tested using air traffic data over a one-hour period. The study evaluated the optimal, worst, and average fitness scores by adjusting the weights' importance orders and comparing the results to the current state-of-the-art method. The results demonstrate that GA with a smaller weighting yields optimum operating costs, highlighting the efficiency of the hybrid approach in resolving the airspace sectorization problem while adhering to geometric constraints. |
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| AbstractList | This paper presents a genetic optimization approach for airspace sectorization that combines K-means clustering and a genetic algorithm (GA). The algorithm minimizes the operating costs related to airspace complexities, unbalanced workloads, and inter-sector coordination requirements while respecting geometric constraints such as minimum distance, convexity, and connectivity. The algorithm initiates sectorization solutions using K-means clustering and then iteratively refines them using GA operations. A comparative analysis was conducted on ten tests, assigning various weights to cost components. The hybrid approach was tested using air traffic data over a one-hour period. The study evaluated the optimal, worst, and average fitness scores by adjusting the weights' importance orders and comparing the results to the current state-of-the-art method. The results demonstrate that GA with a smaller weighting yields optimum operating costs, highlighting the efficiency of the hybrid approach in resolving the airspace sectorization problem while adhering to geometric constraints. |
| Author | Lagha, Mohand Addala, Manel Mogtit, Abdessamed |
| Author_xml | – sequence: 1 givenname: Manel surname: Addala fullname: Addala, Manel email: addala_manel@univ-blida.dz organization: Aeronautical and Spatial Studies Institute,Aeronautical Sciences Laboratory,Blida,Algeria – sequence: 2 givenname: Abdessamed surname: Mogtit fullname: Mogtit, Abdessamed email: Mogtit.Abdessamed@etu.univblida.dz organization: Aeronautical and Spatial Studies Institute,Aeronautical Sciences Laboratory,Blida,Algeria – sequence: 3 givenname: Mohand surname: Lagha fullname: Lagha, Mohand email: laghamohand@univ-blida.dz organization: Aeronautical and Spatial Studies Institute,Aeronautical Sciences Laboratory,Blida,Algeria |
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| Snippet | This paper presents a genetic optimization approach for airspace sectorization that combines K-means clustering and a genetic algorithm (GA). The algorithm... |
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| SubjectTerms | airspace sectorization balanced workload Clustering algorithms Complexity theory Costs genetic algorithm Genetic algorithms Genetics kmeans clustering algorithm Optimization Reliability Reproducibility of results Shape Stability analysis |
| Title | Hybrid Approach for Airspace Sectorization Based on K-Means Clustering and Genetic Algorithm Optimization |
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