基于军民融合的全局飞行流量协同优化方法

With the rapidly continuing growth in demand for flight activities and the increasing airspace usage conflicts, the global optimization of air traffic flow management has become an essential approach to reduce flight delays, decrease flight risk and ensure airspace operation safety. As a typical area of civil-military integration development, air traffic management needs the uniform and efficient integration optimization of the civil and military aviation flight plans. The global optimization of air traffic flow management problem is a complex real-world optimization problem due to its large-scale and multi-objective, and nonseparable characteristics. This paper presents a civil-military integration flight flow multi-objective optimization-CMI model, which considers the difference in civil and military flight plans, the efficiency and safty of sector network, and the civil and military controllers operating features. In order to resolve the unbalance and inadequacy problem lying in population evolution process, a dynamic adaptive multi-objective genetic algorithm (DA-MOGA), which designs the dynamic adjustment mechanism of crossover and variation based on the crowding distance and diversity, is proposed in this paper. The validation results based on the actual data from the sector networks in China show that the DA-MOGA outperforms the two well-known multi-objective evolutionary algorithms.