An improved algorithm based on differential evolution algorithms

dynamic multi-group self-adaptive differential evolution algorithm with local search (DMSDELS)

is applied to optimize functions in this paper.In DMSDELS

the population is randomly and dynamically divided into multi-group individuals

which can exchange information.To speed up search

in the mutation phase the best individual is chosen as the base vector

and the selection of the direction for difference vector is benefit to search.The scaling factor F and the crossover rate CR are self-adapted in order to balance the local search and the global search.To accelerate the convergence

elitist individuals could search in local after they explored specified generations.DMSDELS is tested on thirteen complex benchmark functions.The results are compared with those of other seven algorithms.The results show that DMSDELS is better in the search precision

convergence property and has strong ability to escape from the local sub-optima.