An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework
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 pa...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/302615 |
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| _version_ | 1832562383626698752 |
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| author | Xiangmin Guan Xuejun Zhang Yanbo Zhu Dengfeng Sun Jiaxing Lei |
| author_facet | Xiangmin Guan Xuejun Zhang Yanbo Zhu Dengfeng Sun Jiaxing Lei |
| author_sort | Xiangmin Guan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1b998214feef4b34891c7adc83ef0fd9 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-1b998214feef4b34891c7adc83ef0fd92025-02-03T01:22:47ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/302615302615An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization FrameworkXiangmin Guan0Xuejun Zhang1Yanbo Zhu2Dengfeng Sun3Jiaxing Lei4School of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907-2023, USASchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaConsidering 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.http://dx.doi.org/10.1155/2015/302615 |
| spellingShingle | Xiangmin Guan Xuejun Zhang Yanbo Zhu Dengfeng Sun Jiaxing Lei An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework The Scientific World Journal |
| title | An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework |
| title_full | An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework |
| title_fullStr | An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework |
| title_full_unstemmed | An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework |
| title_short | An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework |
| title_sort | airway network flow assignment approach based on an efficient multiobjective optimization framework |
| url | http://dx.doi.org/10.1155/2015/302615 |
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