Differential evolution with nonlinear simplex method and dynamic neighborhood search

In this paper, by combination of some approaches we propose a new approach of Differential Evolution (DE) algorithm, called DE with nonlinear simplex method and dynamic neighborhood search (DENNS). In our approach the nonlinear simplex method (NSM) is used for population initialization and local neighborhood search. Moreover, local and global neighborhood search operators are employed to generate high quality candidate solutions. During the search process, the population is periodically ranked to change the topology of neighbors. Experimental studies are conducted on a comprehensive set of benchmark functions. Simulation results show that DENNS achieves better results on the majority of test functions, when comparing with some other similar evolutionary algorithms.