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
Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high ...Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high computational complexity. A hybrid algorithm was developed to solve the problem, in which an artificial immune clonal algorithm (AICA) makes use of the global search ability to search the optimal results and simulated annealing (SA) algorithm employs certain probability to avoid becoming trapped in a local optimum. The results obtained from the computational study show that the proposed algorithm is a feasible and effective method for capacitated vehicle routing problem.展开更多
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
基金Supported by the Basic Scientific Research Projects of the Central University of China(ZXH2010D010)the National Natural Science Foundation of China(60979021/F01)~~
文摘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.
文摘Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high computational complexity. A hybrid algorithm was developed to solve the problem, in which an artificial immune clonal algorithm (AICA) makes use of the global search ability to search the optimal results and simulated annealing (SA) algorithm employs certain probability to avoid becoming trapped in a local optimum. The results obtained from the computational study show that the proposed algorithm is a feasible and effective method for capacitated vehicle routing problem.
基金Supported by the National Natural Science Foundation of China (No. 61003198, 60703108, 60703109, 60702062,60803098)the National High Technology Development 863 Program of China (No. 2008AA01Z125, 2009AA12Z210)+1 种基金the China Postdoctoral Science Foundation funded project (No. 20090460093)the Provincial Natural Science Foundation of Shaanxi, China (No. 2009JQ8016)
文摘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).
