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...
| Published in: | Scientific World Journal Vol. 2015; pp. 302615 - 302616 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Wiley-Blackwell
1/1/2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109603802&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109603802 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 1/1/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109603802 NLM26180840 2013105567 10.1155/2015/302615 NLM26180840 PMC4477252 109603802 ppf: 302615 ppct: 1 formats: tig: atl: An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework. aug: au: Guan, Xiangmin Zhang, Xuejun Zhu, Yanbo Sun, Dengfeng Lei, Jiaxing affil: 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. sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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