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

Differential Evolution (DE) is a popular and efficient continuous optimization techniquebased on the principles of Darwinian evolution. Asynchronous Differential Evolution is aDE generalization that allows to regulate the synchronization mechanism of the algorithmby tuning two additional parameters. This paper, after providing a further experimentalanalysis of the impact of the DE synchronization scheme on the evolution, introducesthree self-adaptive techniques to handle the synchronization parameters. Moreover theintegration of these new regulatory synchronization techniques into state-of-the-art (self)adaptive DE schemes are also proposed. Experiments on widely accepted benchmarkproblems 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 onlineautomated tuning process.