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| 1 | Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval显示文摘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 reasonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimization 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 proposed 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 introduced 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, multiobjective genetic algorithm(MOGA), multi-objective evolutionary algorithm based on decomposition(MOEA/D), CC-based multi-objective algorithm(CCMA) as well as other two MPEAs with different migration interval strategies. | Zhang Xuejun Guan Xiangmin Zhu Yanbo Lei Jiaxing | 2015 | Chinese Journal of Aeronautics2015，28，2： | 6 |
| 2 | An evolutionary computational framework for capacity-safety trade-off in an air transportation network显示文摘Airspace safety and airport capacity are two key challenges to sustain the growth in Air Transportation. In this paper, we model the Air Transportation Network as two sub-networks of airspace and airports, such that the safety and capacity of the overall Air Transportation network emerge from the interaction between the two. We propose a safety-capacity trade-off approach,using a computational framework, where the two networks can inter-act and the trade-off between capacity and safety in an Air Transport Network can be established. The framework comprise of an evolutionary computation based air traffic scenario generation using a flow capacity estimation module(for capacity), Collision risk estimation module(for safety) and an air traffic simulation module(for evaluation). The proposed methodology to evolve air traffic scenarios such that it minimizes collision risk for given capacity estimation was tested on two different air transport network topologies(random and small-world) with the same number of airports. Experimental results indicate that though airspace collision risk increases almost linearly with the increasing flow(flow intensity) in the corresponding airport network, the critical flow depend on the underlying network configuration. It was also found that, in general, the capacity upper bound depends not only on the connectivity among airports and their individual performances but also the configuration of waypoints and mid-air interactions among conflicts. Results also show that airport network can accommodate more traffic in terms of capacity but the corresponding airspace network cannot accommodate the resulting traffic flow due to the bounds on collision risk. | Md.Murad HOSSAIN Sameer ALAM Daniel DELAHAYE | 2019 | Chinese Journal of Aeronautics2019，32，4： | 5 |
| 3 | 航空流网络解构与空域资源动态配置研究——以京-成空中廊道为例显示文摘空中廊道作为一种新型航空流组织与空域资源分配方式,是一种复杂的航空流网络,解析其结构特征并评估其结构性能是实现空域资源动态配置的前提。该文通过构建网格时空图方法,以京—成空中廊道为例进行航空流网络解构研究,包括基于航迹簇的网络构型、流量时空波动与航空流聚合分化三方面,据此提出空域资源动态配置方案。研究结论如下:1)以多种邻近阈值指示航迹交叉角度持续增大,检测出航空流网络存在平行流、交叉流、全覆盖流和半覆盖流4种构型,基于其占比和特征的显著差异性提出并行扩容调整方案;2)以高频数网格点状或短线状集中分布指示流量时空波动,检测出航空流网络存在两个不同内涵的时间高峰、以距离中值区为近似对称轴的空间高峰以及在区段间漂移的时空交互高峰,进而提出时间错峰调整方案;3)以网格属性指示聚合分化点的分布,检测出航空流网络阶梯递减、高值集聚和叠合特征,相应提出动态快速分化分流调整方案。 | 董雅晴 路紫 张一诺 冯富晶 闫宇航 | 2022 | 地理与地理信息科学2022，38，1： | 1 |
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