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Recently, technologies on reducing energy consumption of data centers have drawn considerable attentions. One constructive way is to improve energy efficiency of servers. Aiming at this goal, we propose a new energy-aware optimization model based on the combination of data placement and task scheduling in this paper. The main contributions are: (1)The impact of servers' performance on energy consumption...
To measure the performance or validity of clustering algorithms, several evaluation values, such as successful rate, successful number and full successful rate are defined. In order to ensure each cluster to at least contain one vector data, and to maximize several proposed evaluation values, two class assignment algorithms are designed. To testify their performance, we employ them to the k-means...
Constrained optimization problems (COPs) are converted into a bi-objective optimization problem first, and a novel fitness function based on achievement scalarizing function (ASF) is presented. The fitness function adopts the valuable properties of ASF and can measure the merits of individuals by the weighting distance from the ndividuals to the reference point, where the reference point and the weighting...
Differential Evolution(DE) is a kind of simple but powerful evolutionary optimization algorithm with many successful applications. However, it has some weaknesses, especially the slow convergence speed because of weak local search ability in its stochastic search. To overcome the drawback, we first employ the orthogonal design method with quantization technique to generate the initial population,...
Multiobjective bilevel linear programming is a decentralized decision problem, it consists of many objectives at the upper level and the lower level, respectively. It has a wide field of applications and has been proven to be NP-hard. In this paper, a kind of multiobjective bilevel convex programming(MBCP) is studied, in which the lower level is first transformed into an equivalent single objective...
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