Self-regulated differential evolution for real parameter optimization

摘要

This paper presents a novel differential evolution (DE) framework with a self-regulated neighborhood, termed self-regulated differential evolution (SrDE), for real parameter optimization. The novelty and advantage of SrDE are to present a self-regulated neighborhood (SrN) for learning, regulating, and using the neighborhood information of the population in guiding the search process of DE. Specifically, SrDE is characterized by the following three aspects. First, a one-dimensional self-organizing map (SOM) method is employed to dynamically construct the neighborhood relationships between individuals. Second, a self-sizing technique is applied to adaptively regulate the neighborhood size of each individual based on its search state. Third, a neighborhood path-assisted strategy is proposed to utilize promising neighborhood information for guiding the mutation process. Extensive experiments on a suite of real-parameter functions and real-world problems have demonstrated the superior and competitive performance of SrDE when compared with the state-of-the-art DE variants.