Estimation of distribution and differential evolution cooperation for real-world numerical optimization problems

During the last decade, a large number of evolutionary algorithmic variants have been proposed for diverse optimization tasks, most of which are practical engineering applications. In the previous research, one variant is always designed for one specific engineering application. In IEEE Congress on Evolutionary Competition, the numerical optimization competition is held to benchmark different optimization algorithms for more general applications. In this paper, we conduct the optimization method estimation of distribution and differential evolution (ED-DE) by implementing a two-stage ensemble idea, whose effectiveness and efficiency has been experimentally verified.