Improved Differential Evolution with Local Search
Li He, Jun Tang · Journal of Convergence Information Technology · 2012
Differential evolution (DE) is a popular meta-heuristic optimizer which has shown good performance in solving many real-life and benchmark optimization problems. However, DE usually shows slow convergence rate at the last stage of the evolution. To enhance the performance of DE, this paper proposed an improved DE variant (OLSDE) which employs opposition-based concept and local search strategy. Experimental studies on several benchmark functions demonstrate that our approach outperforms standard DE and other two improved DE algorithms.