An improving Differential Evolution Algorithm
Hao Gao · Computer and Information Technology · 2011
Differential evolution algorithm(DE) has better search performance for many optimization problems with few control parameters,easy to use and robust when it is compared with other evolutionary algorithms.But the local search ability of DE is weak.For conquering this drawback of DE,a differential evolution algorithm based local mutation(LMDE) is proposed in this paper.Then the improved algorithm shows fast convergence rate.Numerical study is carried by using benchmark functions.Experiment simulations show that the proposed algorithm has powerful optimizing ability,good stability and higher optimizing precision,so it can be applied in optimization problems.