A Differential Evolution Algorithm Based on Local Search and Boundary Reflection for Global Optimization

Deferential evolution (DE) is a relatively simple dvolutionary algorithm (EA), which is an effective adaptive algorithm in the global optimization domain. In view of the two shortcomings of DE, the low convergence rate in the late evolution and easy to be trapped into the local optimums, a new DE based on local search and boundary reflection (LR-DE) is proposed in this paper. Local search is tantamount to speed up the convergence rate in the late evolution, boundary reflection is to maintain the diversity of the population. Therefore, DE is less able to fall into the local optimums in probability. The new LR-DE are used to some benchmark problems and the results are compared with of other algorithms. The results show that the LR-DE has superior performance in solving optimization problems.