Improved Differential Evolution Algorithm and its Application in Complex Function Optimization

When solving complex function optimization problem,Differential evolution(DE)algorithms may suffer from low convergence rate.In this paper,we propose an improved differential evolution algorithm named n-IDE.Our algorithm uses Gaussian sequence to dynamically generate zoom factors and applies an improved hybrid mutation strategy to individuals in order to improve the overall performance.We compare n-IDE with existing DE approaches using benchmark functions and the experimental result shows that n-IDE has significant improvement on the convergence rate.
Function Optimization Differential evolution Hybrid Mutation Gaussian sequence
XiaoGang Dong Yan Liu ChangShou Deng
School of Information Science and Technology,Jiujiang University,Jiujiang 332005
国际会议
长沙
英文
3698-3701
2014-05-31(万方平台首次上网日期,不代表论文的发表时间)