An Improved Differential Evolution Alogorithm for Optimization

Abstract

Differential Evolution (DE) is an efficient approach capable of handling non-differentiable, non-linear and multi-model objective functions. However, in convergence speed and global optimization, there is still much room for DE to be improved. In this paper, double best mutation operation and chaos Differential Evolution are proposed to improve DE algorithm’s optimized performance. The simulated cases show modified differential evolution algorithm has rapid convergence speed and strong steadiness.