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OPTIMIZATION OF AIRPORT TAXIING PLANNING DURING CONGESTED HOURS BASED ON IMMUNE CLONAL SELECTION ALGORITHM 被引量:1
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作者 柳青 吴桐水 宋祥波 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2012年第3期294-301,共8页
In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical j... In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm(ICSA) is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served(FCFS) strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations. 展开更多
关键词 aircraft taxiing schedule airport operation control hub airport congested hours immune clonal selection algorithm(ICSA)
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CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA
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作者 Gou Shuiping Feng Jing Jiao Licheng 《Journal of Electronics(China)》 2010年第4期474-479,共6页
The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with... The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA). 展开更多
关键词 Projection Pursuit (PP) immune clonal selection algorithm (ICSA) Genetic algorithm (GA) K-means clustering
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