JADE: Self-adaptive differential evolution with fast and reliable convergence performance

Abstract

A new differential evolution algorithm, JADE, is proposed to improve the rate and the reliability of convergence performance by implementing a new mutation strategy ‘DE/current-top-best’ and controlling the parameters in a self-adaptive manner. The °DE/current-top-best’ is a generaliza tion of ‘DE/current-to-best’. It diversifies the population but still inherits the fast convergence property. Self-adaptation is benefi cial for performance improvement. Also, it avoids the require ment of prior knowledge about parameter settings and thus works well without user interaction. Compared to other self-adaptive DE algorithms, JADE converges faster and reliably in at least 10 out of a set of 13 benchmark problems and shows competitive results in other cases as well. Simulations results also clearly show that there is no single parameter value suitable for various problems or even at different optimization stages of a single problem.