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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Published in:2024 International Conference of the African Federation of Operational Research Societies (AFROS) pp. 1 - 5
Main Authors: Addala, Manel, Mogtit, Abdessamed, Lagha, Mohand
Format: Conference Proceeding
Language:English
Published: IEEE 03.11.2024
Subjects:
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
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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.
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
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  givenname: Abdessamed
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  givenname: Mohand
  surname: Lagha
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  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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