Differential Evolution Algorithms with Cellular Populations

Differential Evolution (DE) algorithms are efficient Evolutionary Algorithms (EAs) for the continuous optimization domain. There exist a large number of DE variants in the literature. In this paper, we analyze the effect of adding a cellular structure to the population of some of the most outstanding existing ones. The original algorithms will be compared versus their equivalent versions with cellular population both in terms of accuracy and convergence speed. As a result, we conclude that the cellular versions of the algorithms perform, in general, better than the equivalent state-of-the-art ones in the two considered issues.