A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its referenc...A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle.The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence.In order to reduce the probability of trapping into a local optimal value,an extremum mutation was introduced into simplex-PSO and simplex-PSO-t(simplex-PSO with turbulence) was devised.Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t,and the experimental results confirmed the conclusions:(1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality;(2) compared PSO with chaos PSO(CPSO),the best optimum index increases by a factor of 1×102-1×104.展开更多
The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in nuinerous applications such as analyzing popular spots based on check-in location info...The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in nuinerous applications such as analyzing popular spots based on check-in location information and mining valuable items from shopping records. However, this poses a privacy threat when directly publishing this information. Differential privacy (DP), as a notable paradigm for strong privacy guarantees, is thereby adopted to publish all column counts. Prior studies have verified that truncating records or grouping columns can effectively improve the accuracy of published results. To leverage the advantages of the two techniques, we combine these studies to further boost the accuracy of published results. However, the traditional penalty function, which measures the error imported by a given pair of parameters including truncating length and group size, is so sensitive that the derived parameters deviate from the optimal parameters significantly. To output preferable parameters, we first design a smart penalty function that is less sensitive than the traditional function. Moreover, a two-phase selection method is proposed to compute these parameters efficiently, together with the improvement in accuracy. Extensive experiments on a broad spectrum of real-world datasets validate the effectiveness of our proposals.展开更多
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProject(20070533131) supported by Research Fund for the Doctoral Program of Higher Education of China
文摘A simplex particle swarm optimization(simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the high dimensionality functions.In simplex-PSO,the velocity term was abandoned and its reference objectives were the best particle and the centroid of all particles except the best particle.The convergence theorems of linear time-varying discrete system proved that simplex-PSO is of consistent asymptotic convergence.In order to reduce the probability of trapping into a local optimal value,an extremum mutation was introduced into simplex-PSO and simplex-PSO-t(simplex-PSO with turbulence) was devised.Several experiments were carried out to verify the validity of simplex-PSO and simplex-PSO-t,and the experimental results confirmed the conclusions:(1) simplex-PSO-t can optimize high-dimension functions with 200-dimensionality;(2) compared PSO with chaos PSO(CPSO),the best optimum index increases by a factor of 1×102-1×104.
基金the National Natural Science Foundation of China (Grant Nos. 61433008, 61472071 and U143520006)the Fundamental Research Funds for the Central Universities of China (161604005 and 171605001)the Natural Science Foundation of Liaoning Province (2015020018).
文摘The count of one column for high-dimensional datasets, i.e., the number of records containing this column, has been widely used in nuinerous applications such as analyzing popular spots based on check-in location information and mining valuable items from shopping records. However, this poses a privacy threat when directly publishing this information. Differential privacy (DP), as a notable paradigm for strong privacy guarantees, is thereby adopted to publish all column counts. Prior studies have verified that truncating records or grouping columns can effectively improve the accuracy of published results. To leverage the advantages of the two techniques, we combine these studies to further boost the accuracy of published results. However, the traditional penalty function, which measures the error imported by a given pair of parameters including truncating length and group size, is so sensitive that the derived parameters deviate from the optimal parameters significantly. To output preferable parameters, we first design a smart penalty function that is less sensitive than the traditional function. Moreover, a two-phase selection method is proposed to compute these parameters efficiently, together with the improvement in accuracy. Extensive experiments on a broad spectrum of real-world datasets validate the effectiveness of our proposals.
