Differential Evolution with Neighborhood Search

Abstract

In order to improve the ability of neighborhood search of differential evolutionary (DE) algorithm, we propose a new variant of DE with linear neighborhood search, called LiNDE, for global optimization problems (GOPs). LiNDE employs a linear combination of triple vectors taken randomly from evolutionary population. The main characteristics of LiNDE are less parameters and powerful neighborhood search ability. Experimental studies are carried out on a benchmark set, and the results show that LiNDE significantly improved the performance of DE.