基于GATS贝叶斯网络结构学习的航班延误模型

Flight delay model based on GATS Bayesian Networks structure learning

对遗传算法(GA)贝叶斯网络(BN)结构学习和禁忌搜索算法(TS)进行分析,提出遗传禁忌搜索贝叶斯网络结构学习算法GATS_BNSL。把禁忌搜索思想引入到遗传算法BN结构学习由父代种群产生后代种群的演化过程中,以禁忌搜索交叉和禁忌搜索变异改进传统的遗传算子,对比实验分析表明了GATS_BNSL的学习优势。应用此方法,基于真实数据,建立了大型枢纽机场航班离港延误模型。该模型切实反映了导致航班延误的多因素之间的因果关系,而且建模时间少,学习正确率高。

Based on the study of Bayesian Networks structure learning by Genetic Algorithm（GA） and Tabu Search（TS）, GATS_BNSL, the algorithm of Bayesian Networks structure learning by hybrid GA and TS, is put forward. The method of TS is applied into evolution of populations to descendant populations of Bayesian Networks structure learning based on GA. Instead of crossover and mutation, the TS crossover and TS mutation are suggested. Contrastive experimental results show the learning advantage of GATS-BNSL. Meanwhile, this method is applied to build the flight delay model of a large hub airfield by using real data. Causality of multi-factors that lead to flight delays are displayed with less learning time and higher learning accuracy.