摘要

An artificial immune network (AIN), AINFCM, has been successfully used for fuzzy clustering to overcome the shortage of FCM algorithm that is sensitive to the selection of initial centres. However, as a stochastic searching algorithm, the runtime of AINFCM goes up especially when dealing with large quantities of data or generating much more antibodies for clone selection. In this paper, the PAINFCM is proposed for parallel affinity calculation of antibodies according to time complexity of AINFCM algorithm. Subsequently, a coarse-grained version of PAINFCM algorithm was proposed to parallelize clone expansion. Experiments indicated that the PAINFCM improves efficiency of AIN. Furthermore, it provides a good balance between global searching ability and run time of the AIN.