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An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework.

Guan X, Zhang X, Zhu Y, Sun D, Lei J - ScientificWorldJournal (2015)

Bottom Line: Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it.Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability.Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China ; National Key Laboratory of CNS/ATM, Beijing 100191, China ; Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing 100191, China.

ABSTRACT
Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

No MeSH data available.


Related in: MedlinePlus

(a) Airspace sectors in China. (b) Flights operation in China.
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fig1: (a) Airspace sectors in China. (b) Flights operation in China.

Mentions: The national route network of China consists of 1706 airway segments, 940 waypoints, and 150 airports. Note that the takeoff and landing phases of flights are truncated within a given radius (usually 10 NM) around airports. The traffic around airports is managed with specific procedures by the terminal control area (TCA) control services in these zones. The airspace is divided into many sectors, and Figure 1(a) shows the sectored airspace in China. The air traffic data was extracted from flight schedule database (FSD) of the summer in 2009 released by Civil Aviation Administration of China (CAAC). In order to better describe the difference between the algorithms' performances, we consider two scenarios: 960 flights (the busiest one hour) and 1664 flights (the busiest three hours).


An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework.

Guan X, Zhang X, Zhu Y, Sun D, Lei J - ScientificWorldJournal (2015)

(a) Airspace sectors in China. (b) Flights operation in China.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4477252&req=5

fig1: (a) Airspace sectors in China. (b) Flights operation in China.
Mentions: The national route network of China consists of 1706 airway segments, 940 waypoints, and 150 airports. Note that the takeoff and landing phases of flights are truncated within a given radius (usually 10 NM) around airports. The traffic around airports is managed with specific procedures by the terminal control area (TCA) control services in these zones. The airspace is divided into many sectors, and Figure 1(a) shows the sectored airspace in China. The air traffic data was extracted from flight schedule database (FSD) of the summer in 2009 released by Civil Aviation Administration of China (CAAC). In order to better describe the difference between the algorithms' performances, we consider two scenarios: 960 flights (the busiest one hour) and 1664 flights (the busiest three hours).

Bottom Line: Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it.Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability.Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China ; National Key Laboratory of CNS/ATM, Beijing 100191, China ; Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing 100191, China.

ABSTRACT
Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

No MeSH data available.


Related in: MedlinePlus