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Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

引用

Chinese Journal of Aeronautics 2015年第2期28卷 556-563页

作者： Zhang Xuejun Guan Xiangmin Zhu Yanbo Lei Jiaxing School of Electronic and Information Engineering Beihang University National Key Laboratory of CNS/ATM Beihang University Aviation Data Communication Corporation

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by rea- sonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimiza- tion problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm （MPEA） framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is pro- posed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution （CC） algorithm combined with non-dominated sorting genetic algorithm II （NSGA-II） is intro- duced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multi- objective genetic algorithm （MOGA）, multi-objective evolutionary algorithm based on decom- position （MOEA/D）, CC-based multi-objective algorithm （CCMA） as well as other two MPEAs with different migration interval strategies.

关键词： Air traffic flow management Cooperative co-evolution Dynamic migration intervalstrategy flight assignment Parallel evolution algorithm

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

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Science China(Information Sciences) 2016年第7期59卷 185-201页

作者： Renli L Xiangmin GUAN Xueyuan LI Inseok HWANG Department of General Aviation Civil Aviation Management Institute of China School of Electronic and Information Engineering Beihang University School of Aeronautics and Astronautics Purdue University

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 ***, 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.

关键词： air traffic flow management flight assignment multi-island parallel evolution algorithm migration topology cooperative co-evolution

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