Parameter analysis of negative selection algorithm

Enhancing network cluster synchronization capability based on artificial immune algorithm

With the deeper study on complex networks, more and more attention has been paid to the research on the cluster synchronization phenomena based on complex networks. In the real world, synchronization phenomena or cluster synchronous behaviors occur frequently, some of which may result in larger negative impacts to the society, such as “cadmium rice event,” while others bring significant economic benefits to the society, such as the synchronization of the propaganda for “black Friday.” Therefore, research on cluster synchronism has great values for theoretical study and social applications. Currently, the study of cluster synchronicity is focused on the solution of the synchronization threshold and the analysis of the synchronization phenomenon, etc. However, the optimization to enhance the synchronous evolutionary effect is rarely presented in literatures. To overcome these limitations of current work, we explore the optimization of network structure with artificial immune algorithms under the condition of a constant network scale and finally realize the promotion of synchronous evolution effect in this paper. Moreover, the relevant research results are applied to real cases. On one hand, for the positive synchronous behaviors, the network structure with good synchronization capability is created to achieve better synchronization. On the other hand, the connection between nodes and edges in the network is cut off to avoid the occurrence of negative synchronous behaviors.