复杂网络社区结构划分日益成为近年来复杂网络的研究热点,到目前为止,已经提出了很多分析复杂网络社区结构的算法。但是大部分算法还存在一定的缺陷,而且有些算法由于其时间复杂度的过高导致其不适合应用于对大型网络的分析。提出了一...复杂网络社区结构划分日益成为近年来复杂网络的研究热点,到目前为止,已经提出了很多分析复杂网络社区结构的算法。但是大部分算法还存在一定的缺陷,而且有些算法由于其时间复杂度的过高导致其不适合应用于对大型网络的分析。提出了一种基于PSO微粒群算法的复杂网络社区结构分析方法。此方法无需预先知道组成该复杂网络的社区数量、社区内的节点数以及任何门限值。该算法的可行性用Zachary Karate Club和College Football Network模型进行验证。展开更多
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
文摘复杂网络社区结构划分日益成为近年来复杂网络的研究热点,到目前为止,已经提出了很多分析复杂网络社区结构的算法。但是大部分算法还存在一定的缺陷,而且有些算法由于其时间复杂度的过高导致其不适合应用于对大型网络的分析。提出了一种基于PSO微粒群算法的复杂网络社区结构分析方法。此方法无需预先知道组成该复杂网络的社区数量、社区内的节点数以及任何门限值。该算法的可行性用Zachary Karate Club和College Football Network模型进行验证。
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.
