Crowd evolution method based on intelligence level clustering

Purpose – The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual's ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm. Design/methodology/approach – This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents' intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm. Findings – Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments. Practical implications – The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents. Originality/value – This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.