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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

Comparison of PEA and PEA without DAO for 960 flights (a) and 1664 flights (b).
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig3: Comparison of PEA and PEA without DAO for 960 flights (a) and 1664 flights (b).

Mentions: Furthermore, like the first experiment, the nondominated solutions of the algorithms are shown in Figure 3. It shows that PEA performs much better than the other method and its nondominated solutions can dominate the solutions obtained by PEA without DAO. For the scenario of 1664 flights, PEA has the most nondominated solutions and spreads nicely in the objective space.


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)

Comparison of PEA and PEA without DAO for 960 flights (a) and 1664 flights (b).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Comparison of PEA and PEA without DAO for 960 flights (a) and 1664 flights (b).
Mentions: Furthermore, like the first experiment, the nondominated solutions of the algorithms are shown in Figure 3. It shows that PEA performs much better than the other method and its nondominated solutions can dominate the solutions obtained by PEA without DAO. For the scenario of 1664 flights, PEA has the most nondominated solutions and spreads nicely in the objective space.

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