The utility of scale factor adaptation in differential evolution

Differential evolution (DE) is a simple, yet often effective, approach to real-parameter optimisation. Despite this simplicity, successfully applying DE to a specific problem typically requires careful calibration of several parameters. To simplify the application of DE to problems, researchers have turned to self-adaptive methods, which attempt to learn the `ideal´ DE configuration during the evolutionary process. This paper investigates one particular type of self-adaptive DE, self-adaptive neighbourhood search differential evolution (SaNSDE), with a particular focus on the adaptation of the scale factor generation operators. The results presented in this paper suggest that SaNSDE´s scale factor generation method may be replaced with a simpler, non-adaptive approach without degrading search performance.