基于混合SFLA-PSO算法网络谣言辟谣方法研究OA

To address the challenges of rapid rumor dissemination and the tendency of traditional algorithms to fall into local optima in social networks,this paper proposes a real-time rumor refutation method based on a hybrid Shuffled Frog Leaping Algorithm and Particle Swarm Optimization(SFLA-PSO).This approach integrates the clustering cooperative local search capability of SFLA with the fast global convergence advantage of PSO.Firstly,a rumor dissemination model incorporating us-ers'social ties and trust degrees is constructed.Users are categorized into different age groups,and differentiated modeling strategies(SFLA or PSO)are applied according to their cognitive characteristics.Secondly,the SFLA-PSO hybrid optimiza-tion algorithm is designed to dynamically update user trust levels through local deep search and global information interac-tion,thereby efficiently blocking rumor propagation.Simulation results demonstrate that,compared with standalone SFLA and PSO algorithms,the proposed hybrid algorithm exhibits significant advantages in convergence speed and computational efficiency.It can guide user cognition towards the truth more rapidly and comprehensively,effectively curbing the spread of rumors.