Demonstrator selection in a social learning particle swarm optimizer

Social learning plays an important role in behavior learning among social animals. Different from individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without the extra costs of individual trial-and-error. Inspired by the natural social learning phenomenon, we have transplanted the social learning mechanism into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants, the SL-PSO is performed on a sorted swarm, and instead of merely learning from historical best positions, the particles are able to learn from anyone better (demonstrators) in the current swarm. A key mechanism in the SL-PSO is the learning strategy, where an imitator will learn from different demonstrators. However, in our previous work, little discussion has been focused on demonstrator selection, i.e., which demonstrators are to learn from by the imitator. In this paper, based on the analysis of the demonstrator selection in the SL-PSO, two demonstrator selection strategies are proposed. Experimental results show that, the proposed demonstrator selection strategies have significantly enhanced the performance of the SL-PSO in comparison to five representative PSO variants on a set of benchmark problems.