Abstract
Particle swarm optimization(PSO) algorithm is a kind of optimization algorithm. It is widely used because of its high efficiency, but it also has limitations. It performs poorly when facing the high-dimensional problems or local optimal. In this paper, a new Class social learning particle swarm optimization algorithm (termed CSLPSO) is proposed to improve the precision and convergence speed of the PSO algorithm. In the human society, the inferior individuals learn from the excellent individuals, while the excellent individuals learn more from their own experience, and those in the lower class are generally more difficult to learn from the higher class. According to these phenomena, we design a particle swarm optimization (PSO) algorithm that mimics the human society. The results show that CSLPSO has a good performance by comparing it with other PSO varieties on a set of 11 benchmark functions.
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