Particle Swarm Optimization with Social Influence

At present, particle swarm optimization (PSO) and its variants have been proven to be useful methods to solve complicated optimization problems. However, PSO and most of its variants only consider the impact of global best position and personal historical best position, which results in an issue of insufficient convergence and diversity. In this paper, PSO with social influence (PSOSI) is proposed to deal with this issue. Specifically, three strategies are employed in PSOSI. Firstly, each particle will choose two exemplars as its “social learning” part including the global best particle and its best companion particle, which brings more useful knowledge for each particle. Besides, gravity coefficient is introduced to describe the influence caused by the exemplars, enhancing the diversity of population while ensuring the best experience being shared. In addition, each particle further learns from its best companion particle on each dimension, realizing global to local variable-scale search, and enhancing the overall convergence ability. Moreover, 10 state-of-the-art PSO variants and 3 other typical optimization algorithms are com-pared with it on 28 benchmark functions of CEC2013 test suite. The experimental results demonstrate the superiority of PSOSI