Sequential DE enhanced by neighborhood search for Large Scale Global Optimization

In this paper, the performance of a sequential Differential Evolution (DE) enhanced by neighborhood search (SDENS) is reported on the set of benchmark functions provided for the CEC2010 Special Session on Large Scale Global Optimization. The original DENS was proposed in our previous work, which differs from existing works which are utilizing the neighborhood search in DE, such as DE with neighborhood search (NSDE) and self-adaptive DE with neighborhood search (SaNSDE). In SDENS, we focus on searching the neighbors of individuals, while the latter two algorithms (NSDE and SaNSDE) work on the adaption of the control parameters F and CR. The proposed algorithm consists of two following main steps. First, for each individual, we create two trial individuals by local and global neighborhood search strategies. Second, we select the fittest one among the current individual and the two created trial individuals as a new current individual. Additionally, sequential DE (DE with one-array) is used as a parent algorithm to accelerate the convergence speed in large scale search spaces. The simulation results for twenty benchmark functions with dimensionality of one thousand are reported.