Social learning differential evolution

Abstract

Differential evolution (DE) has attracted much attention in the field of evolutionary computation and has proved to be one of the most successful evolutionary algorithms (EAs) for global optimization. Mutation, as the core operator of DE, is essential for guiding the search of DE. In this study, inspired by the phenomenon of social learning in animal societies, we propose an adaptive social learning (ASL) strategy for DE to extract the neighborhood relationship information of individuals in the current population. The new DE framework is named social learning DE (SL-DE). Unlike the classical DE algorithms where the parents in mutation are randomly selected from the current population, SL-DE uses the ASL strategy to intelligently guide the selection of parents. With ASL, each individual is only allowed to interact with its neighbors and the parents in mutation will be selected from its neighboring solutions. To evaluate the effectiveness of the proposed framework, SL-DE is applied to several classical and advanced DE algorithms. The simulation results on forty-three real-parameter functions and seventeen real-world application problems have demonstrated the advantages of SL-DE over several representative DE variants and the state-of-the-art EAs.