  Differential Evolution (DE) is recognized as one of the most powerful
optimizers in the evolutionary algorithm (EA) family. Many DE variants were
proposed in recent years, but significant differences in performances between
them are hardly observed. Therefore, this paper suggests a multi-layer
competitive-cooperative (MLCC) framework to facilitate the competition and
cooperation of multiple DEs, which in turns, achieve a significant performance
improvement. Unlike other multi-method strategies which adopt a
multi-population based structure, with individuals only evolving in their
corresponding subpopulations, MLCC implements a parallel structure with the
entire population simultaneously monitored by multiple DEs assigned to their
corresponding layers. An individual can store, utilize and update its evolution
information in different layers based on an individual preference based layer
selecting (IPLS) mechanism and a computational resource allocation bias (RAB)
mechanism. In IPLS, individuals connect to only one favorite layer. While in
RAB, high-quality solutions are evolved by considering all the layers. Thus DEs
associated in the layers work in a competitive and cooperative manner. The
proposed MLCC framework has been implemented on several highly competitive DEs.
Experimental studies show that the MLCC variants significantly outperform the
baseline DEs as well as several state-of-the-art and up-to-date DEs on CEC
benchmark functions.
