Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-04-01 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the quantification of data privacy and data utility in privacy protection, based on the basic principles of metric space and norm, this paper proposed a privacy and data utility metric model. First, it gave the data numerical processing method. The data was converted into a matrix for calculation. Secondly, it introduced a privacy preference function to measure the change of sensitive attributes over time. Then, it analyzed the privacy protection model and quantified the data changes generated by the privacy protection technology. Finally, this paper built a metric space, and gave privacy amount, data utility and privacy protection calculations. Simulation experiments show that the established metric model can effectively reflect the amount of private information.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-28 Cooperative journals: 《计算机应用研究》
Abstract： In order to improve the performance of whale optimization algorithm for solving complex function optimization problems, this paper proposed an improved whale optimization algorithm based on adaptive parameters and niche technology. Firstly, the algorithm introduced an adaptive probability threshold to coordinate the exploration and exploitation ability. Then, the algorithm used adaptive position weights to adjust the whale position update formula to improve the convergence speed and search precision. Finally, the algorithm used preselection niche technology to avoid premature convergence. The results on 12 typical benchmark functions shows that the improved algorithm has faster convergence speed and higher search precision than other comparison algorithms. It proves that the improvement strategy can effectively improve the performance of the whale optimization algorithm for solving complex function optimization problems.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-28 Cooperative journals: 《计算机应用研究》
Abstract： However, there are still some shortcoming in cuckoo search algorithm(CS) , such as low convergence precision, slow convergence speed, Weak search vitality and interference phenomena among dimensions when dealing with high-dimensional optimization problems. Dynamically Adaptive Cuckoo Search Algorithm Based on Dimension by Opposition-based Learning(DA-DOCS) was proposed, Firstly, the selected solution updated for dimension-by-dimension by Opposition-based Learning, this result reduced interdimensional interference and expanded population diversity. Then the method of elite retention was used to evaluate the results and improve the search ability of the algorithm. Finally, the information of the current solution was fully utilized to dynamically adaptive the scaling factor control to guide the solution to converge quickly and enhance the search vitality of the algorithm. The experimental results show that compared with the standard cuckoo search algorithm, the proposed algorithm has improved convergence precision, convergence speed and search vitality. Compared with other improved algorithms, it has certain competitive advantage.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-10-11 Cooperative journals: 《计算机应用研究》
Abstract： The accuracy of traffic flow forecasting plays an important role in the field of Intelligent Transportation Systems. In order to improve the accuracy of traffic flow forecasting model based on Least Squares Support Vector Machine, this paper proposed a novel modified gravitational search algorithm (TCK-AGSA) for parameters optimization. Firstly, this paper improved the Kbest function based on Tent map, so that the algorithm has a mechanism to jump out of local optimum. Then, by introducing the guidance of global optimal to accelerate the movement of agents towards optimal solution. Furthermore, it introduced the evolutionary factor and converge factor into the weighted coefficient of agent’s velocity to make the algorithm more adaptive. The simulation results for 12 benchmark functions show that the performance of TCK-AGSA is better than GSA and its variants. Finally, this paper proposed a LSSVM model optimized by TCK-AGSA, and selected the 2016 actual traffic flow data of Guizhou Expressway for experiment. The results show that the proposed model has better prediction accuracy, robustness, and generalization capability.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-10-11 Cooperative journals: 《计算机应用研究》
Abstract： In order to solve the disadvantage of the traditional whale optimization algorithm, which is slow convergence and easy to fall into local optimum, this paper proposed a mixed strategy based whale optimization algorithm. Firstly, it introduced the nonlinear adjustment strategy to modify the convergence factor, balance the exploration and exploitation capability and accelerate the convergence speed. Then, by introducing an adaptive weighted coefficient into the position update formula of whales to improve the search precision of the algorithm. Finally, it combined the limit threshold idea of artificial bee colony algorithm to effectively jump out of the local optimum and prevent premature convergence. The results show that the proposed algorithm has better search precision and convergence speed through experiments on different dimensions of 14 benchmark functions.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》
Abstract： In order to solve the problem that the clustering result of K-means algorithm gets affected by the initial cluster centers easily, this paper proposed a novel K-means clustering algorithm based on improved gravitational search algorithm. Firstly, it enhanced the global exploration and local exploitation capability of the algorithm with the introduction of adaptive concept to control the attenuation factor of gravitational constant. Then, by introducing immune clonal selection algorithm to make the algorithm jump out of the local optimum efficiently. The experimental results on twelve test functions prove the effectiveness and superiority of the improved GSA. Finally, by combining the improved GSA with K-means algorithm, this paper proposed a new clustering algorithm called A2F-GSA-Kmeans. The experimental results on six test datasets show that the algorithm has better clustering quality.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-19 Cooperative journals: 《计算机应用研究》
Abstract： Microblog platform contains potential user's information, through microblog data mining microblog user interest has important social significance. On account of the characteristics of user interest and microblog information, this paper put forward a method of microblog user interest mining based on text clustering and interest decay(TCID-MUIM) . Firstly, it used the synonyms combined strategy based on Tongyici Cilin to make up for the process of modeling the lack of word frequency information. Secondly, it used the double single-pass incomplete clustering algorithm to make up the problem that the microblog text was shorter so that difficult to dig the topic information. Finally, it used the LDA model modeling, as well as considering the user's interest changes with time, by introduction of time factor compresses the microblog-topic matrix into the user-topic matrix to gain user interest. Experimental results show that compared to traditional modeling methods and the modeling methods of merger user's all history microblog as the same document, the TCID-MUIM method presented which modeling results have a higher topic's differences and closer to the user's real interest preferences.
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