Improved differential evolution algorithm and its application in complex function optimization
Xiaogang Dong, Yan Liu, Changshou Deng · 2014
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.