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SCIENCE CHINA Information Sciences, Volume 59, Issue 7: 072201(2016) https://doi.org/10.1007/s11432-015-5495-3

A large-scale flight multi-objective assignment approach based on multi-island parallel evolution algorithm with cooperative coevolutionary

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  • ReceivedMay 23, 2015
  • AcceptedSep 30, 2015
  • PublishedMay 26, 2016

Abstract

Due to the rapid increase of air traffic demand, the large-scale flight assignment plays a crucial role in reducing airspace congestion and economic losses via reasonably regulating the air traffic flow of China. In this paper, the large-scale flight assignment problem is formulated as a multi-objective model with consideration of the reduction of airspace congestion and flight delay. However, it is a large-scale combinatorial optimization problem with complex constraints and tightly coupled decision variables, which is difficult to deal with. Hence, an effective multi-objective optimization algorithm is proposed based on the multi-island parallel evolution framework (PEA) with a left-right probability migration topology. Multi-island PEA employs multiple evolution populations for solving the problem simultaneously, and the left-right probability migration topology for exchange individuals among populations to improve the efficiency of the cooperation of populations. Then the cooperative co-evolution (CC) algorithm is introduced for each population to further improve the searching capability. Simulation results using the real traffic data from the 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 multi-objective genetic algorithm, the well-known multi-objective evolutionary algorithm based on decomposition, a CC-based multi-objective algorithm as well as other two parallel evolution algorithms with different migration topologies.


Funded by

National Natural Science Foundation of China(U1433203)

Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61221061)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. U1433203), and Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 61221061).


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