A study on the synchronization behaviour of Differential Evolution and a self-adaptive extension

Differential Evolution (DE) is a popular and efficient continuous optimization technique based on the principles of Darwinian evolution. Asynchronous Differential Evolution is a DE generalization that allows to regulate the synchronization mechanism of the algorithm by tuning two additional parameters. This paper, after providing a further experimental analysis of the impact of the DE synchronization scheme on the evolution, introduces three self-adaptive techniques to handle the synchronization parameters. Moreover the integration of these new regulatory synchronization techniques into state-of-the-art (self) adaptive DE schemes are also proposed. Experiments on widely accepted benchmark problems show that the new schemes are able to improve performances of the state-of-theart (self) adaptive DEs by introducing the new synchronization parameters in the online automated tuning process.