终端区飞机排序的混合人工鱼群算法

为了保障飞行安全, 对终端区着陆飞机进行有效的排序, 建立了以航班延误总时间最小为目标函数的规划模型, 以人工鱼群算法为基础, 融合了遗传算法的选择操作和模拟退火算法的依概率接受的思想, 形成混合人工鱼群算法, 对着陆飞机排序问题进行了仿真计算, 并与先到先服务算法、模拟退火算法以及蚁群算法进行了对比研究。仿真结果表明: 与先到先服务相比, 使用人工鱼群算法使得单跑道、双跑道延误分别减少了9.3%和48.0%, 计算时间小于3s;与蚁群算法和模拟退火算法相比, 求解的延误与时间最小, 因此, 提出的混合算法可行。

Mixed artificial fish school algorithm of aircraft sequencing in terminal area

In order to ensure flight safety and effectively sequence landing aircrafts in terminal area, an object model with minimum total delay was developed, the ideas of selection operation in genetic algorithm (GA) and the acceptance according to probability in simulated annealing (SA) algorithm were considered, a mixed algorithm was proposed based on artificial fish school algorithm (AFSA), the sequence problem of landing aircraft was solved, and its computational result was compared with the ones computed by first-come-first-serve(FCFS) algorithm, SA algorithm and ant colony optimization (ACO) algorithm. Simulation result shows that the total delays are respectively reduced by 9.3% and 48.0% for single and double runways compared with FCFS algorithm, computational time is less than 3 s, while the delay and computational time are least compared with SA algorithm and ant colony optimization algorithm, so the mixed algorithm (MA) is feasible. 3 tabs, 12 refs.