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In this paper we investigate several self-adaptive mechanisms to improve our previous work on NSDE, which is a recent DE variant for numerical optimization. The self-adaptive methods originate from another DE variant, SaDE, but are remarkably modified and extended to fit our NSDE. And thus a self-adaptive NSDE (SaNSDE) is proposed to improve NSDEpsilas performance. Three self-adaptive mechanisms are utilized in SaNSDE: self-adaptation for two candidate mutation strategies, self-adaptations for controlling scale factor F and crossover rate CR, respectively. Experimental studies are carried out on a broad range of different benchmark functions, and the proposed SaNSDE has shown significant superiority over NSDE.
Yang et al. (Sun,) studied this question.