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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

The framework of the proposed algorithm.
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alg1: The framework of the proposed algorithm.

Mentions: With the aim of avoiding prematurity and improving the convergence rate of this complex problem, a new multiobjective optimization framework is proposed. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed and one-way ring migration topology for exchange individuals among populations is applied to improve the efficiency of the cooperation of populations. Secondly, the multiobjective evolutionary algorithm NSGA2 is used to optimize each population. In addition, this paper introduces the idea of cooperative coevolutionary (CC) into the resolution of the multiobjective ANFA problems. The main idea is to divide the high-dimensional problem into low-dimensional subcomponents. The subcomponents work cooperatively to obtain better solutions. Finally, all the nondominated solutions are stored in an archive. As the ANFA problem is of such a large scale with thousands of variables, the main difficulty is how to maintain the diversity of the population to get better solutions and to improve the distribution of the solutions in the archive. Hence, 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. The framework is presented in Algorithm 1.


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)

The framework of the proposed algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

alg1: The framework of the proposed algorithm.
Mentions: With the aim of avoiding prematurity and improving the convergence rate of this complex problem, a new multiobjective optimization framework is proposed. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed and one-way ring migration topology for exchange individuals among populations is applied to improve the efficiency of the cooperation of populations. Secondly, the multiobjective evolutionary algorithm NSGA2 is used to optimize each population. In addition, this paper introduces the idea of cooperative coevolutionary (CC) into the resolution of the multiobjective ANFA problems. The main idea is to divide the high-dimensional problem into low-dimensional subcomponents. The subcomponents work cooperatively to obtain better solutions. Finally, all the nondominated solutions are stored in an archive. As the ANFA problem is of such a large scale with thousands of variables, the main difficulty is how to maintain the diversity of the population to get better solutions and to improve the distribution of the solutions in the archive. Hence, 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. The framework is presented in Algorithm 1.

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