A binary differential evolution algorithm learning from explored solutions

Although differential evolution (DE) algorithms have shown great power in solving continuous optimization problems, it is still a challenging task to design an efficient binary variant of DE algorithm. In this paper, we propose a binary learning differential evolution (BLDE) algorithm, which can efficiently search the feasible region by learning from the obtained solutions. Meanwhile, we also define a refinement metric and a renewal metric to depict the exploitation ability and the exploration ability of evolutionary algorithms (EAs), respectively. Theoretical analysis validates the convergence of BLDE, and numerical results demonstrate its efficiency on the benchmark problems. Comparisons of the refinement metric and the renewal metric show that they can evaluate the exploitation and exploration abilities to some extent.