Premature convergence happens in Particle Swarm Optimization (PSO) partially due to improper search information propagation. Fast propagation of search information will lead particles get clustered together quickly. Determining a proper search information propagation mechanism is important in optimization algorithms to balance between exploration and exploitation. In this paper, we attempt to figure out the relationship between search information propagation and the population diversity change. Firstly, we analyze the different characteristics of search information propagation in PSO with four kinds of topologies: star, ring, four clusters, and Von Neumann. Secondly, population diversities of PSO, which include position diversity, velocity diversity, and cognitive diversity, are utilized to monitor particles' search during optimization process. Position diversity, velocity diversity, and cognitive diversity, represent distributions of current solutions, particles' “moving potential”, and particles' “moving target”, respectively. From the observation of population diversities, the effect of search information propagation on PSO's optimization performance is discussed at last.