基于粒子群寻优模型的思想政治教育策略研究

Ideological and Political Education Strategies: A Particle Swarm Optimization Model

粒子群优化算法(PSO)是模拟鸟群的捕食行为,寻找食物的最优策略是搜寻目前离食物最近的周围区域。受PSO模型启发,对大学生的群体意识行为进行建模,并在模型中引入正负激励措施,引入正激励,优于不引入任何激励,引入负激励措施只能令大学生群体远离负面典型,而不能帮助他们进入最优意识状态。针对该激励效果,积极引入正负激励的工作策略,并谨慎单独使用负激励措施,创新工作思路,实行正负激励反向运用,以改进思想政治教育工作策略。

Particle swarm optimization algorithm(PSO) is simulating the predation of birds.The optimal strategy to search for food is finding closest food around birds.Inspired by PSO,a college students' behaviour of group consciousness model has been established.Positive and negative incentive policy has been introduced in the model.The introduction of positive incentives proves to be better than the scant introduction,while the introduction of negative incentives can cause students to keep off the negative stereotype but cannot help them to converge to the optimal state of consciousness.Based on the findings,implications have been suggested that both positive and negative incentives strategies can be used with different purposes in the ideological and political education work.