会议专题

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

第26届中国控制与决策会议(2014 CCDC)

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)