A Dynamic Mutation PSO algorithm and its application in the Neural Networks

摘要：

This paper presents a dynamic mutation particle swarm optimization(DMPSO).The particle swarm optimization(PSO)is a popular swarm algorithm,which has exhibited good performance on many optimization problems.However,similar to other swarm intelligence algorithms,PSO also suffers from premature convergence.Sub swarm and mutation are widely used strategies in the PSO algorithm to overcome the premature convergence.In the DMPSO,firstly,the population is divided into two groups,and some special operations – dynamic crossover and dynamic mutation are employed in the evaluation.The DMPSO algorithm was used in the training process of neural network,and compared to other three different types of NN training algorithms,and the experiment results show that the new algorithm performances better on two representative types of application of neural network.

关键词： Swarm Intelligence PSO Neural Networks Dynamic Mutation

作者: Wei Gao Hai Zhao Jiuqiang Xu Chunhe Song

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）