Analysis of Network Topology and Information Propagation Based on Particle Swarm Optimization

In this article, PSO (Particle swarm optimization algorithm) algorithm is effectively combined with network topology and information propagation model, and an analysis method of network topology and information propagation based on improved PSO algorithm is proposed, which provides new ideas and methods for network optimization design. By comparing the algorithm performance under different network topologies and information dissemination models, the effectiveness and superiority of PSO algorithm are verified by experiments. The simulation results show that the average fitness value of PSO algorithm is obviously better than that of genetic algorithm (GA) and simulated annealing algorithm in the late iteration, and its value is closer to 1. Moreover, PSO algorithm is also less than GA and simulated annealing algorithm in the quantity of iterations needed to find the optimal solution. This means that PSO algorithm is more efficient and converges faster in solving network optimization problems. This shows that the fast convergence and excellent optimization ability of PSO algorithm make it have a wide application prospect in the fields of network topology and information dissemination analysis. The research in this article provides valuable reference for the research in this field.