A social learning particle swarm optimization algorithm for scalable optimization

Social learning plays an important role in behavior learning among social animals. In contrast to individual (asocial) learning, social learning has the advantage of allowing individuals to learn behaviors from others without incurring the costs of individual trials-and-errors. This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants where the particles are updated based on historical information, including the best solution found by the whole swarm (global best) and the best solution found by each particle (personal best), each particle in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter control method. The proposed SL-PSO is first compared with five representative PSO variants on 40 low-dimensional test functions, including shifted and rotated test functions. The scalability of the proposed SL-PSO is further tested by comparing it with five state-of-the-art algorithms for large-scale optimization on seven high-dimensional (100-D, 500-D, and 1000-D) benchmark functions. Our comparative results show that SL-PSO performs well on low-dimensional problems and is promising for solving large-scale problems as well.