人工鱼群—粒子群混合算法优化进港航班排序

针对空中交通管理中的进港航班排序问题, 提出了人工鱼群—粒子群混合算法(AFPSO)这一航班排序算法来优化进港航班排序, 使时段内进港航班队列总延误时间最少。算法结合了基本人工鱼群算法(AFSA)和基本粒子群算法(PSO)各自的优点, 先以AFSA在全局寻找满意的解域, 再以PSO算法在这些解域中进行快速的局部搜索获得精确解, 最终使算法提高收敛速度和搜索精度。仿真结果表明, 在单跑道和双跑道情况下, AFPSO算法使得航班队列总延误时间比FCFS调度方法减少了20. 9%和34. 4%, 比基本AFSA减少了3. 2%和3. 5%。算法得到的满意解能够为自动化空中交通管理提供实时支持。

Hybrid algorithm with artificial fish swarm-particle swarm optimization for arrival sequencing and scheduling optimization

Faced with the aircraft landing scheduling in the air traffic management, this paper proposed a hybrid algorithm (AFPSO) for scheduling arrival aircrafts with the least total delay. AFPSO algorithm combined the advantages both of artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), searched satisfactory solution domain by AFSA and searched exact solution by PSO. The simulation results show that the AFPSO decreases the total delay time for single and double runways by 20. 9% and 34. 4% compare with first-come-first-serve (FCFS), by 3. 2% and 3. 5% compare with AFSA. A satisfactory solution of this AFPSO can provide real-time support for automatic air traffic management.