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2-median location improvement problems under weighted l_1 norm and l_∞ norm on trees 被引量:1
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作者 杨利平 关秀翠 《Journal of Southeast University(English Edition)》 EI CAS 2013年第3期346-351,共6页
This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices... This paper focuses on the 2-median location improvement problem on tree networks and the problem is to modify the weights of edges at the minimum cost such that the overall sum of the weighted distance of the vertices to the respective closest one of two prescribed vertices in the modified network is upper bounded by a given value.l1 norm and l∞norm are used to measure the total modification cost. These two problems have a strong practical application background and important theoretical research value. It is shown that such problems can be transformed into a series of sum-type and bottleneck-type continuous knapsack problems respectively.Based on the property of the optimal solution two O n2 algorithms for solving the two problems are proposed where n is the number of vertices on the tree. 展开更多
关键词 2-median network improvement problem TREE knapsack problem l1 norm l∞ norm
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Women's Status Improved but Problems Linger
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《China Population Today》 2001年第5期11-15,共5页
关键词 THAN Women’s Status Improved but problems Linger
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A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model
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作者 Yiqing YU 《International Journal of Technology Management》 2015年第4期126-128,共3页
In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF n... In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power. 展开更多
关键词 the trajectory of floats RBF neural network model Global optimization model 0-1 knapsack problem improved geneticalgorithm
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